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Perceptions of African American English Dialect Density by
Anglo-European American Speech-Language Pathologists

presented by

Gregory C. Robinson

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PERCEPTIONS OF AFRICAN AMERICAN ENGLISH DIALECT DENSITY BY
ANGLO-EUROPEAN AMERICAN SPEECH-LANGUAGE PATHOLOGISTS

By

Gregory C. Robinson

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY

Audiology and Speech Sciences

2006

ABSTRACT

PERCEPTIONS OF AFRICAN AMERICAN ENGLISH DIALECT DENSITY BY
ANGLO-EUROPEAN AMERICAN SPEECH-LANGUAGE PATHOLOGISTS

By
Gregory C. Robinson

Language researchers have used the terms dialect density and dialect rate to
describe the varying amounts and types of features used by speakers of a given dialect
(Oetting & McDonald. 2002). Members of the general population use other terms. such
as “thick dialect" or “slight accent.“ These colloquial terms often coincide with
subjective statements about how easy the speaker was to understand and/0r how
noticeable the use of the dialect was. Although dialects consist of features across all
levels of language (phonology. morphosyntax. semantics. and pragmatics). phonological
features may be the most identifiable (Wolfram & Schilling-Estes. 1998). Little is known
concerning the actual role that these features play in perception of inter-speaker
differences.

It is important to understand the perceptibility of dialect density. because speakers
of some dialects are penalized for the use oftheir dialect in some situations. For
example. a speech-language pathologist (SLP) may mistake an African American English
(AAE) dialect for a speech-language disorder. This may be more likely to happen with
speakers who use a greater number or particular types of AAE phonological features.
This study aims to determine the extent to which selected amounts and types ofAAE
features contribute to subjective judgments by non-AAE-speaking speech-language
pathologists in predominantly Anglo-European American school districts. The subjective

judgments under investigation are perceived degree of comprehensibility (i.e.. how easily

understandable the speaker would be to the general population) and perceived dialect
detectability (i.e.. how noticeable the use of AAE was) as perceptual indicators of dialect
density.

Certified speech-language pathologists from predominantly Anglo-European
American school districts in rural mid-Michigan listened to individual sentences that
contained varying amounts and types of AAE phonological features. The SLPs rated
each sentence on five-point ordinal scales regarding how noticeable the dialect was (1:
AAE dialect was not noticeable. 5: AAE dialect was extremely noticeable) and how
understandable the speaker would be to people in the general population (1: Very
difficult to understand, 5: Very easy to understand). The ratings for the different
sentences were compared to determine the extent to which the frequency and types of
features represented in the sentences contributed to the SLPs’ judgments.

The study found that both comprehensibility and dialect detectability ratings were
affected by the number of features included in the sentences. The type of features
included had consistent effects in the comprehensibility ratings but less consistent effects
in the dialect detectability ratings. Possible explanations for these inconsistencies were
discussed. Theoretical implications for prototype-based models of speech perception
were addressed along with practical implications for speech-language pathologists in the
areas of: (a) assessments with nonmainstream dialect speakers. and (b) accent

modification.

AC KNOWLEDGMENTS

This dissertation would not have been possible without the invaluable input and
support from several people. First. I would like to thank my advisor, Dr. Ida Stockman.
She constantly supported. motivated, and inspired me during this great undertaking. She
has changed my life by imparting her knowledge and determination during this process.
Next. I would like to thank Dr. Dennis Preston, who helped not only to give me excellent
advice through the development of this idea but also shared his unfailing joy of research.
language diversity. and social activism. I also thank Dr. Peter Lapine and Dr. Brad
Rakerd for their intellectual contributions and support throughout the process.

The logistics of the study would not have been possible without the support of
Cheryl Granzo. Director of Therapy in the Ionia Intermediate School District who helped
me contact study participants. Dr. Laura Smith-Olinde shared her knowledge of statistics
to assist me with data analysis. I also thank Johanna and Derrick Boult, Mary Jo
Hidecker. Robin Pulford, Diane Ogiela. and Rana Alkhamra for their input and support
during my entire doctoral program.

Last, but certainly not least. I want to thank my family. My daughter. Madeleine.
kept me on track by making me little sticky notes of encouragement reading. "Get it dun
(sic)” and “work, work. work. disertashen (sic).” My wife, Monica. was always there to
encourage me, push me to work harder, and force me to take breaks. She is my soul-mate
and my best friend. I could not have done this without her. I also want to thank my
parents, Lewis and Carolyn Robinson. who were behind me 11 %. and my parents in-

law, Alan and Phyllis C lark, for their support throughout this process.

TABLE OF CONTENTS

LIST OF TABLES ...................................................................
LIST OF FIGURES ..................................................................
CHAPTER I: INTRODUCTION ................................................

A. Temrinology ............................................................
B. Two Perspective on Normal Language Variation: Folk vs. Linguistic
1. From Categorical to Gradient View of Dialect Features.
2. Attitudes Toward Non-mainstream Dialects ................
C. Dialect Detectability and C omprehensibility ................
1. Dialect Detectability ...................................
2. Comprehensibility .....................................
a. Phonemic Difference ................................
b. Listener Familiarity ..................................
D. Statement of the Problem ..............................................
1. SLPs as Participants ...........................................
a. SLPs as Professionals with Power ..................

b. Professional Mandates Pertaining to
Linguistic Diversity .................................

c. Antiquated Paradigms Concerning Language

and Culture ...........................................
d. SLPs and Cross-Dialectal Assessments .............
e. SLPs and Dialect C ornprehensibility Judgments...
f. SLPS and Dialect Detectability Judgments ......
2. AAE as a Language Variety .........................................
a. Description of AAE .......................................

xi

xiii

ll

14

14

16

19

b. The Misdiagnosis of AAE as Disordered Language...
c. AAE Phonology ............................................
3. F requeney and Type of Features as Variables ...............
a. Frequency of Features Included .............................
b. Types of Features Included ...............................
E. Research Questions .......................................................
CHAPTER 11: METHOD ............................................................
A. Participants ................................................................
1. Characteristics ....................................................
2. Recruitment ......................................................
3. Human Subjects Approval ......................................
B. General Description of the Stimuli ..........................................
C. Construction of the Stimuli .................................................

1. Number of Features Included .....................................

IQ

. Type ofDialect Features Included ...............................
a. Phonological Patterns .....................................

b. Perceptual Salience Groupings .........................

DJ

. Description of the Sentences ...................................

4‘s.

. Preparation of the Stimulus Recordings .........................
3. Speakers” Characteristics .................................
b. Speakers’ Conditions ......................................

c. Construction of Naturalness Rating Stimulus C Ds..

(It

. Verifying Naturalness of Stimulus Sentences .................

vi

40

41

44

44

45

49

51

51

51

56

57

57

58

a. Naturalness Rater Characteristics ......................... 68
b. Naturalness Rating Task .................................. 68
c. Naturalness Rating Results ................................ 69

6. Randomization and C ounterbalancing of Stimulus Sentences. 70

a. Speaker Order ................................................ 71

b. Task Order .................................................... 72

c. Sentence Randomization ................................... 72

D. Collection ofData ............................................................ 73
1. Presentation ofStimuli ......................................... 73

2. Rating Tasks ......................................................... 74

a. Task Orientation and Familiarization ..................... 74

b. C omprehensibility ........................................... 76

c. Dialect Detectability ........................................ 79

E. Intra-rater Reliability ......................................................... 80
F. Data Processing ............................................................. 80
G. Data Analysis ................................................................ 8O
1. Compilation Comparisons ......................................... 80

2. Task-Specific Comparison of Test Variables .................. 81
CHAPTER III: RESULTS ................................................................ 83
A. Intra-rater Reliability .......................................................... 84
B. Compilation Comparisons ............................................... 85
1. Compilation Comparisons Over Both Tasks ....................... 86

2. Comparisons Specific to C omprehensibility ................... 87

vii

3. C omparisons Specific to Dialect Detectability ................... 88

4. Compilation Groupings Resulting from Compilation

Differences ............................................................ 90
C. Principal Findings: Dialect Detectability Ratings ...................... 91
1. Description of Factors ............................................. 91
2. High Detectability Group Results ............................... 93
3. Frequency Effects .......................................... 93
b. Salience Effects ........................................... 95
c. Frequency - Salience Interaction Effects .............. 95
3. Low Detectability Group Results ............................... 95
a. Frequency Effects ........................................ 95
b. Salience Effects ........................................... 100
c. Frequency by Salience Interaction .................... 100
4. Post Hoc Comparison of Features for Detectability Ratings ...... 101
a. Ranking of Features ...................................... 101
b. Perceptual Salience Classification and Detectability. 103
5. Summary of Dialect Detectability Results ..................... 104
D. Principal Findings: C omprehensibility Ratings ........................ 105
1. Frequency Effects .................................................. 105
2. Salience Effects ..................................................... 107
3. Frequency by Salience Interaction ............................ 108
4. Summary of C omprehensibility Results .......................... 108
E. Written Comments from Listeners ....................................... 109
CHAPTER IV: DISCUSSION ........................................................... llO

viii

A. Gradient Perception of AAE Dialect by SLPs ........................... 110

1. Frequency of Dialect Features ..................................... 110

2. Perceptual Salience of Dialect Features ........................... 111

a. Comprehensibility Judgments ............................. 1 11

b. Dialect Detectability Judgments .......................... 1 14

c. Summary of Perceptual Salience Contribution ......... 117

B. Methodological Variables .................................................. 120
1. Differences Among Compilations .................................. 120

2. Differences Between Speakers ..................................... 123

C. Theoretical and Research Implications ................................... 123
1. Stevens Law and General Sensory Perception .................. 123

2. Johannson’s Factors for Non-native Accent Feature

Perceptibility .................................................... 124
3. Steriade’s P-Map ................................................. 125
4. Prototype-Based Models of Speech Perception ................. 127
5. Implications for Research Methodology ............................ 128
D. Practical Implications ......................................................... 129
1. Implications for SLPs in the Public Schools ..................... 130
2. Implications for Instructors of SLPs ............................. 132
3. Implications for SLPs Engaging in Accent Modification
Therapy ....................................................... 132
E. Study Limitations and Future Research .................................... I33
1. Listener Variations ................................................... 133
2. Stimulus Variations ................................................... 133

3. Task Variations ....................................................... 134

4. Speaker Variations .................................................... 135

F. Conclusions ...................................................................... 136
APPENDICES ........................................................................... 13 7
REFERENCES ............................................................................ 157

LIST OF TABLES

1a. Listing of Stimulus Sentences and Targets for Planned Alternations 54

1b. Listing of Stimulus Sentences and Targets for Planned Alternations

Phonetically Transcribed ................................................. 55
2. Characteristics of 6 groups of sentences presented during

comprehensibility ratings ............................................... 56
3. AAE Phonological Patterns Included in the Stimuli .................... 58

4. Ranking of Phonological Patterns for Perceptual Salience in
Nonsymbolic Units ........................................................ 6O

5. The Recording Source of Each of the Stimulus Sentence Constituents 66
6. Speaker Order and Task Order for Each Stimulus Compilations. 71

7. Differences Among the Four Compilations Over Both Perceptual
Rating Tasks ............................................................. 87

8. Differences Among the Four Compilations Specific to the Dialect
Detectability Task ....................................................... 89

\O

. Compilation Characteristics and Rank Ordering According to
Average Dialect Detectability Ratings ................................. 9O

10. Sentences in the High Detectability Group With 0. 1. and 3 Features
Analyzed for Dialect Detectability ..................................... 95

11. Speaker Comparison for the Mean Change in Dialect Detectability
Scores Across the Different Frequency Levels in the Low
Detectability Group ..................................................... 98

12. Compilation Comparison for the Mean Change in Dialect

Detectability Scores Across the Different Frequency Levels in
the Low Detectability Group ........................................... 100

xi

13. Ranking of Features by Mean D Scores .................................. 102

14. Comparison of Mean D Scores for Comparable Features in the High
and Low Salience Groups ................................................ 104

15. Years Experience as SLP for Participants Organized by Stimulus
Compilation ............................................................ I"2

~~

xii

Ix)

LIST OF FIGURES

. Schematic of the Folk and Linguistic Theories of Language ............ 6
. Folk Perceptual Continuum of Gradient (or Variable) Dialect Use 11
. Phonological Severity Continuum From the Hodson Assessment of

Phonological Process-3 ................................................ 3 7

. Example Stimulus Table for C omprehensibility Ratings ................ 77

. Average Dialect Detectability Scores in the High Detectability Group

Obtained in Relation to the Frequency and Salience of AAE
Features .................................................................... 94

. Average Dialect Detectability Ratings for the Two Speakers and the

Two Compilations in the Low Detectability Group Organized by
Frequency and Salience .................................................. 97

. Average Comprehensibility Scores Elicited from Stimuli Obtained

in Relation to the Frequency and Salience of AAE Features ........ 106
. Original Single Dimensional Model of Perceived Dialect Density ....... 118
Multidimensional Model of Perceived Dialect Density .................... 1 19

xiii

CHAPTER I
INTRODUCTION

“Their accent is so thick you'd have to have a knife to cut. like tryin’ to cut
butter." “He had only a trace of an accent, ...but as we drove further South, his accent
got heavier and heavier.” “They have a definite accent. They‘re the ones that are
difficult to understand.” These statements. taken from the documentary “American
Tongues” (Alvarez & Kolker, 1986), are classic examples of the way the general
population describes individual differences between speakers of varying regional dialects.
Many times these descriptions are expressed in metaphorical terms that depict variation
in density or strength (e. g., “thick dialect" or “strong accent”). Language scholars, in
contrast, often use categorical terms, such as idiolects, to refer to differences among
speakers of a given dialect (Brook, 1970).

Recently, however, some scholars have given attention to the more gradient view
of the term dialect. While the variability of speech-language patterns is the focus of
much of the US sociolinguistic research, some researchers have developed methods to
quantify dialect use along a continuum concerning the amount of features used by
different speakers. These scholars have coined the phrases “dialect density” and “dialect
rate” to holistically describe the variable use of dialect by some speakers (for a summary,
see Oetting & McDonald, 2002). These studies of dialect density have been limited by
their focus just on speech production. Little seems to be known about the relationship
between perception of native-language dialect density and the actual speech
characteristics used within native language dialects. This relationship has, however. been

explored more thoroughly among second-language speakers. The inherent goal of

second-language acquisition is often to achieve near-native proficiency. The normal
speaker ofa stigmatized dialect may or may not desire to speak a more standard dialect.
However, many listeners, who speak standard dialects. believe that the speaker ofa
stigmatized dialect should have the goal to speak more in accord with the standard
language variety (Niedzielski & Preston. 2003).

Exploring listener perceptions ofdialect density is important because many
speakers of some dialects are penalized for their speech-language pattems (Niedzielski &
Preston, 2003; Preston & Robinson, 2005; Wolfram & Schilling-Estes, 1998). Speakers,
who have what the general population identifies as a “thick“ dialect. may experience
greater negative social consequences than do those who speak a more standard version of
the same dialect. These negative attitudes may limit access to important resources, such
as housing, jobs, and education for the speakers of stigmatized dialects (Labov, 1966;
Preston & Robinson, 2005; Purnell, Idsardi, & Baugh, 1999; Smith, 1993). These studies
suggest that several factors specific to the listener. rather than the speaker, may
determine the degree to which the speaker is penalized. These factors include the
listener‘s attitude toward the dialect, the experience that the listener has with the dialect,
and the level of social or economic power that the listener may have over the speaker.

Perceptions regarding dialect density have particular relevance for speech-
language pathologists (SLPs; Preston & Robinson, 2005). The SLP is professionally
certified to detect a breakdown in communication. It is the SLP’s job to differentiate
between a breakdown that is attributed to a speech-language disorder and one that results
from his/her own lack of experience with a client’s normal dialect patterns and/or

personal attitudes toward those dialect patterns. However. this distinction is not always

[‘0

easy to make when subjective ratings (such as overall intelligibility) are used in speech-
language assessments. In other words, misdiagnosing a normal language difference as a
disorder may result when normal speakers of a dialect are perceived to be as difficult to
understand as disordered speakers of more standard dialects. Misdiagnosis may also
occur when the SLP’s personal attitudes result in the judgment that the features should be
classified as “incorrect” language.

Such misdiagnoses may explain why African American students are over-
represented on SLP caseloads when compared to the general population (US.
Department of Special Education Programs, 2004). Many of them acquire a social
dialect that is not spoken by most SLPs.. The dialect, commonly referred to as African
American English (AAE), differs from Standard English varieties in many ways. These
differences include phonological, morpho-syntatic, semantic, and pragmatic aspects
(Bailey & Thomas. 1998; Pollock. et a1. 1998; Rickford & Rickford. 2000; Smithennan,
1977; Smitherman, 1994; Stockman, 1996; Terrell & Jackson, 2002. Wolfram &
Schilling-Estes, 1998).

The overwhelming majority (93%) of SLPs are from an Anglo-European
American background (ASHA, 2005). They do not speak AAE and. therefore. may be
especially prone to make a wrong diagnosis (Seymour & Bland, 1991‘). Their
inexperience with AAE could cause them to judge some speakers as difficult to
understand. Their attitudes toward or ignorance about the use of particular dialect
features may further cause them to be reluctant to accept the features as normal language.

Clinical misdiagnoses can have negative consequences. In the public school

setting. for example, a misdiagnosis could influence the way a student is viewed by peers.

teachers, and by him/herself. Such misdiagnosis may be primarily based upon the ability
of the SLP to detect the dialect and/or the inability of the SLP to understand the dialect.

AAE speakers are diverse in the AAE linguistic features that they use. Based
upon the variable use of certain features. some AAE speakers may be more prone to
misdiagnosis than others. This dissertation research aims to determine how the varying
presence of AAE dialect features impacts Anglo-European American public school SLPs’
perceptions of how easy a speaker is to understand and the detectability of the speaker‘s
dialect.

T erminolo gv

The meanings of words are “neither overt nor finite" (Stockman, 2000, p. 340).
The terms used in reference to language variation are no exception. Even though both
linguists and non-linguists use the terms “dialect” and “accent" to describe language
variation, the connotations may differ widely (Malmkjaer. 1991; Wolfram & Schilling-
Estes, 1998). The linguistic distinction between these two words usually involves the
levels of language that are being referred to: “[A dialect is] any user-defined variety [ofa
language], that is, any variety associated with speakers ofa given type, whether
geographically or otherwise defined” (Malmkjaer. 1991. p. 93-94). “The term accent is
generally used in the field [of linguistics] in the strict sense of a variety differing
relevantly from others only in phonological respects, not in grammar or lexis”
(Malmkjaer, 1991, p. 95).

Another distinction is also made by language professionals. The term dialect has

been defined and used by some to denote a variety present in a first language, while

accent is used to denote linguistic features present as a result of a second language
(Nicolosi. Harryrnan. & Kresheck, 2003).

The distinction made by the general population is not always as clear. In fact, the
terms are often used interchangeably (Niedzielski & Preston. 2003'). When non-linguists
are quoted in this dissertation, the term accent may include features related to all levels of
language associated with form, content, and use.

For the purposes of this research, the investigator used the term dialect to denote a
language variety that can be described in terms of particular phonological,
morphosyntactic, semantic, and pragmatic features. Thus. “the phonological features of
the dialect ” can be described as a particular set of the features that characterizes a dialect.
The term accent will be used to refer to the pronunciation features associated with second
language acquisition (i.e., “second-language accent") due to the fact that this is the term
used often in the literature.

Two Perspectives on Normal Language Variation: Folk vs. Linguistic

Understanding perception of normal language variation within the general
population also entails understanding how such variation is viewed in the scholarly
literature. Linguists and the non-linguist general population may not view language
differences in exactly the same way. Specifically, linguists agree that all human language
systems, including dialects. are natural, rule-governed, and hence valid in their own right
(Linguistic Society of America, 1997). This concept is not widely held by the general
population. Preston and Robinson (2005) adapted a taxonomy developed by Niedzielski
and Preston (2003) in which a Linguistic Theory of Language is contrasted with a Folk

(or non-linguist) Theory of Language (see Figure l).

Figure 1. Schematic of The Folk and Linguistic Theories of Language (Niedzielski &

Preston. 2003)

THE LANGUAGE

A Folk Theory
ofLanguage

Good Language

\
h

5
Ordinary Language

 

Dialects "Errors" V

THELANGUAGE
A “Linguistic” Theory
ofLanguage

 
  

Dialectitl Dialect #2 Dialect #3 Etc.

 

ldiolecttil Idiolect#2 Etc.

With respect to the Linguistic T heory ofLanguage, the individual varieties (or
idiolects) are the most concrete realizations of language. The conceptualizations of
c‘lialects/accents and, subsequently, lcmguage are socially real but significantly more
abstract than the actual productions made by speakers. Hence. a language is simply an
abstract representation of idiolects and dialects determined by society to be linguistically

and/or politically similar (Fasold, 1999; Linguistic Society of America. 1997).

F urthermore, each dialect. and even each idiolect. is equally legitimate.

Under the Folk T heory of Language (Niedzielski & Preston. 2003), the most
concrete construct is The Langmge. Only the individuals who speak an idealized form of
a language are viewed as truly speaking a rule-governed system ofcommunication. An
individual is allowed to deviate slightly from this form and still be characterized as
speaking good language. Once a person deviates too much. he/she drifts out of the realm
of real language and into erred speech in which dialects and slips-of-the-tongue are of
equal status. The determination of what is good language tends to correspond to the
variety spoken by those persons who enjoy social prestige. namely those with wealth.
education, and high social status.

This distinction between the linguists" and non-linguists” views of normal
language variation was infomied by interviews with 68 non-linguists residing in
Southeastern Michigan (about topics related to language diversity). The transcripts of
these interviews revealed that many people are very intolerant of stigmatized dialects.
They found them difficult to understand at times and were frequently preoccupied with
dialect features they view as “uneducated" or “incorrect" ways of speaking. The
investigator of this dissertation reviewed the testimonies provided in Niedzielski and
Preston’s (2003) paper and determined a tendency for the informants to discuss inter-
speaker differences in terms of gradient levels of perceived dialect density or strength.
These gradient temrs suggested that the informants were commenting on the degree to
which the given speaker deviated from good language.

From Categorical to Gradient View of Dialect Features

Language researchers recognize that the speakers of any given dialect do not all

use the same number and types of features (Craig. Thompson. Washington, & Potter.
2003; Craig & Washington. 2000; Craig & Washington, 2002; Craig & Washington,
2004; Craig, Washington. & Thompson-Porter, 1998a; Labov, 1966; Oetting &
McDonald, 2001, 2002; Stockman, 1996, 2004; Washington & Craig, 1994, 1998;
Washington, Craig, & Kushmaul. 1998). For example, Craig and colleagues (2003)
reported that African American children produced different amounts and types of dialect
features across grades in Michigan public school districts. These individual speech
patterns are often called idiolects (Brook, 1970).

When members of the general population discuss speakers of various dialects in
the United States (US), they may perceive these inter-speaker language differences
along a continuum or gradient. rather than in a categorical manner. For example. in the
documentary “American Tongues” (Alvarez & Kolker, 1986), a speaker from New
England described the speech of her fiancee as they traveled South to meet his family.
She began by stating that he had “only a trace of a Southern accent” before they began
the trip. She stated that as they drove further South. “his accent got heavier and heavier.”
As is exemplified by this comment. these gradient descriptions of language variation
sometimes take the metaphorical form of increasing amounts of mass or density. For
example, in the same documentary, a speaker from New York stated that the Southem
dialect of Mississippi and Georgia is so thick “you‘d have to have a knife to cut it—like
trying to cut butter.”

Further evidence of non—linguist perception of gradient dialect use was provided
in Niedzielski and Preston‘s (2003') study. An examination of the testimonies provided in

their study (see above) revealed that the informants used multiple methods to convey

dialect density. For example, one informant used the phrase “really, really. really. really.
Southern” (p. 105) to describe the speech of one speaker. Another informant used the
phrase “very strong Southern accent” (I). 107) to describe the speech of another speaker.
The transcripts also revealed contrasting terms, such as “slight Southern accent” (p.110)
and “no accent” (p. 97). apparently to represent the opposite extreme to speaking a
“strong” or “thick” dialect. This continuum may refer to both the frequency and type of
dialect features being used by a speaker. For example, one informant contrasted speakers
from Tennessee with those from “smaller towns in Kentucky” (p. 99). He explained that
“the Southern people from Tennessee... still had an accent but you know. they didn‘t use
as many double negatives,” (pp. 99-100).

Researchers who study language acquisition and disorders have documented that
speakers of non-mainstream dialects produce a variety of different features and with
varying levels of frequency (Craig et al., 2003; Craig & Washington, 2002; Craig &
Washington, 2004; Craig et al., 1998a; Oetting & McDonald. 2001; Stockman, 2004;
Washington & Craig, 1994, 1998; Washington et al., 1998). The terms dialect density or
dialect rate refer to the variable rates at which specific features associated with a given
dialect are present in the speech of individuals. Several methods have been used by
language researchers to measure dialect rate in children. Oetting & McDonald (2002)
investigated and described the different methods of dialect quantification that have been
used in numerous studies (e.g. Craig & Washington, 2000; Oetting et a1, 1997;
Washington & Craig. 1994. 1998). The predominate purpose of the studies cited in
Oetting & McDonald’s (2002) paper was to describe the expressive language of children,

who speak non-mainstream English dialects. These studies have illuminated the diversity

that is present in the language ofchildren who speak non-mainstream dialects and the
factors related to variable use of dialect features.

The audience of these studies was primarily speech-language pathologists (SLPs)
because SLPs must understand the acceptable range of variation within dialects to inform
their diagnostic decisions. However, SLPs must be concerned not only with dialect
production, but also with dialect perception by the SLPs, themselves. It was not within
the scope of these studies to determine the perceptibility of the dialect features. Thus,
they did not provide insight into the basis for the perceived differences in dialect density
or variable use of features within the same dialect.

Based upon the descriptions of variable dialect use provided by the informants in
Niedzielski and Preston’s (2003) work. the investigator of this dissertation’s study
designed a schematic continuum to illustrate a possible model of how non-linguists may
view dialect use (see Figure 2). This continuum is ordinal in nature with “no accent” or
“good speech” at one pole and extreme dialect use at the other. A speaker may move
along the continuum depending on personal experiences. For example. in Niedzielski
and Preston’s study, Speaker B stated:

I developed a Southern accent in Texas... I grew up in the New York City area.

went to Texas for two years. by age eight I had a Texas accent, went back to New

York, I lost it pretty much in about six months... (p. 102).

While it appears that the variable use ofdialect features by a speaker plays a major role in
determining movement along this continuum, it is still unclear how this variability

influences perceptions of dialect use by a listener.

10

Figure 2

Folk Perceptual Continuum of Gradient (or Variable) Dialect Use

 

Very Intelligible Unintelligible
Not Noticeable Very Noticeable
0
No Accent/Dialect Slight Accent/Dialect Thick/

Heavy/

Strong/

Definite

Accent/ Dialect

Attitudes Toward N on-nrainstream Dialects

As is conveyed in Figure 2. references to variable dialect use by people in the
general population tend to coincide with references to negative factors that may inhibit
communication because the speech: (a) is unintelligible or difficult to comprehend
(dialect comprehensibility) and/or (b) diverts the listener‘s attention away from the
message communicated to a focus instead on the dialect itself( dialect detectability).

It is possible that these factors may be influenced by or coincide with the negative
public attitudes toward speakers of some dialects that have been documented by linguists
(Wolfram & Schilling-Estes, 1998). Consider the previous example from the American
Tongues documentary of the New England speaker describing the accent of her fiancee as
getting “heavier and heavier” (Alvarez & Kolker, 1986). She continued by stating that
her fiancée‘s speech "became filled with all these hillbilly kind ofregionalisms. You
know, this real kind of ‘you-all’ stuff.” This speaker reported that she ended her
engagement because she could not bear the thought of giving birth to “Southern-speaking
babies.” In the Niedzielski and Preston (2003) transcripts. informants were documented

often using phrases like “poorer language skills” and references to education level

11

coinciding with discussions about speakers with stigmatized dialects. For example. one
informant stated that “the people from Tennessee seemed to be more up on education
than those from you know the smaller towns in Kentucky” (p.99).

Several studies have examined these negative attitudes toward dialects using a
methodology called the “matched-guise” technique. The practical implications of having
certain negative attitudes toward various dialects are probed implicitly rather than
directly asking respondents to state their attitudes--attitudes that are likely to be overtly
denied by respondents (Adams, 1981; Belton. 1984; de Bardelaben. 1980; Edwards,
1982; Johnson & Buttny, 1982; Labov. 1966; Labov & Ash. 1997; Lambert. Hodgson.
Gardner, & F illenbaum, 1960; Marston, 1974; Mulac & Rudd, 1977; Munro, Derwing. &
Flege, 1999: Preston. 1996b; Purnell et al., 1999; Smith. 1993; Stein. 1981; Voigt. 1994,).
In these studies, the listeners rated speakers of different dialects on subjective categories
that were apparently unrelated to the linguistic features used or not used. These
categories were conceptually grouped in terms of competence and solidarity. Non-
mainstream dialects are often ranked low on the dimension of competence. This category
includesjudgrnents about intellectual fortitude or work ethic. Therefore, speakers of
these stigmatized dialects were commonly judged as “uneducated“ and “lazy”.

C onversely. non-mainstream dialects tended to be rated high on the dimension of
solidarity, invoking characteristics such as “friendliness” and “trustworthiness”. For
example, Preston (1999) asked Michigan respondents to rate the degree to which they felt
different personality traits applied to the residents of the different states in America. The
personality traits corresponded to social status (standardness) and group solidarity

(friendliness). a dichotomy that is analogous to the previously mentioned dichotomy of

“competence” verses “solidarity”. While the Northern and Midwestern states (i.e., the
speakers perceived to have a more mainstream dialect) received high ratings for social
status, the Southern states (i.e.. the more non-mainstream dialect speakers) received
higher ratings for group solidarity. Similar findings were discussed by Edwards (1982),
who used the same variables of social status and group solidarity. Therefore. it should
not always be assumed that speakers of stigmatized dialects will be penalized by others in
every social situation. However. the traits pertinent to social advancement in the broader
US. society (i.e., intelligence, education, and so on) are the ones in which non-
rnainstrearn dialect speakers are commonly perceived to be deficient by the wider
community.

According to Preston and Robinson (2005'), if professionals in positions of
cultural, social, or economic power base expectations of intellectual fortitude on dialectal
characteristics. then considerable social consequences can occur for speakers of non-
mainstrearn dialects. For example, Purnell, Idsardi. and Baugh (1999) revealed that even
relatively subtle linguistic cues can have grave social consequences for speakers. When a
single person uttered the word “hello” in three different dialects to a group of listeners
(Standard American English, AAE. and Chicano English), variation in vowel tenseness
and pitch on the first vowel was enough to trigger identification of the dialects. When the
utterance was expanded to “Hello, I‘m calling about the apartment you have advertised in
the paper,” and presented to actual property owners, the Standard American English
utterances elicited appointments approximately 70% of the time. The AAE and Chicano
English versions (i.e., the non-mainstream dialects) elicited appointments about 30% of

the time. This study suggests that if a speaker ofa stigmatized dialect is evaluated by a

13

person with social power who adheres to the Folk Theory of Language (see above). then
his/her use of a stigmatized dialect may be penalized. The more a speaker uses features
associated with the dialect. especially highly stigmatized features. the more likely could
this be the case. The reason is that the dialect is either highly noticeable or less
intelligible to the listener. as is evident in the testimonials of non-linguists (Alvarez &
Kolker, 1986; Niedzielski & Preston. 2003). Thus the listener attributes this increased
communicative difficulty to the speaker’s communication skills rather than to his/her
own lack of experience with the dialect.
Dialect Dctectahilitv and Comprehensihilit}!

As previously stated, colloquial descriptions of density reflect the degree to which
a dialect is detectable and/or intelligible. A term related to intelligibility is
comprehensibility, or the degree of perceived difficulty in understanding a speaker
(Munro & Derwing. 1995a). The general population commonly makes such perceptual
judgments. For example, in the American Tongues documentary (Alvarez & Kolker.
1986), a Southern speaker commented that New York speakers “have a definite accent.
They’re the ones that are difficult to understand.” The first statement attests to the degree
to which the informant noticed the New York accent. and the second statement conveys
the sense of reduced comprehensibility (i.e., the amount of effort it took for her to
understand the speech of New Yorkers). Furthermore. the construction “They are the
ones” suggests that her own variety has been accused of incomprehensibility. Both
judgments contribute to perceived differences in dialect density as discussed below.
Dialect Detectability

Dialect detectability is the degree to which a dialect is noticed in a speaker.

Detection of a dialect or second language accent could distract from a speaker’s message.
even though the dialect is not difficult to understand. The investigator of this
dissertation‘s study conducted an internet search (Google, 2005) using the keywords
“thick accent” and “thick dialect”. The websites listed identified the extent to which
gradient descriptions ofdialect use corresponded to statements of perceived dialect
density. On one website, professors were reviewed by students. Two students stated that
a professor spoke with a “very thick accent;” however, they said that the professor was
“not very difficult to understand.” Therefore, what the students were referring to in their
use of the phrase “very thick accent” was the degree to which the accent could be
noticed.

Within the second-language acquisition literature. the issue of detectability has
been emphasized. because many non-native speakers of languages desire to achieve near
native-like production. Studies examining non-native speakers of English have used
primarily subjective rating scales for listeners to detect the degree of foreign accent
perceived in speech samples (Brennan & Brennan, 1981a. 1981b; Burda, Scherz,
Hageman, & Edwards, 2003; Derwing & Munro, 1997; F lege & Fletcher, 1992;
Johansson, 1978; Munro & Derwing, 1995a, 1995b; Niedzielski, 1999; Ryan, Carranza.
& Moffie, 1977). F lege and Fletcher (1992) argued that the degree of foreign accent
detected by listeners was influenced by several factors related to both the listeners and the
speakers. One listener effect was the number of native speakers included in the sample.
When large numbers of native speakers were included in the samples. the respondents
tended to rate foreign accented speakers high for accent detectability. This observation

suggests that non-native speech grew in salience when it was in the presence of a large

15

number of native speech samples, perhaps creating an oddball effect. A second listener
effect was related to the familiarity with the sentences. After listeners became familiar
with the sentences, they were more likely to rate the sentences higher in accentedness
than before they were familiar with the sentences. This outcome suggests a cumulative
effect on the ratings. That is, dialect features become more salient the more frequently
they are experienced. Additionally, the findings suggest that after one has already
achieved the linguistic meaning of an utterance, processing load is somehwat relieved so
that attention to accompanying features of the message can be attended to.
C omprehensihil i ty

Sometimes the terms intelligibility and conrprehensibility have been used
interchangeably in the research. For the purposes of this dissertation, both terms relate to
how the words were understood in relation to pronunciation, not in relation to semantics
or syntax. The words differ in that intelligibility refers to whether the words were
successfully recognized or not (i.e., a dichotomous distinction). Con-iprehensibility is
defined as the gradient level of difficulty experienced in recognizing the words (Munro &
Derwing, 1995a). Presumably, this is in reference to the amount of top-down processing
that occurs in understanding a speaker. In other words. a sentence may be intelligible in
that all of the words of the message were successfully decoded as the intended words. At
the same time, the sentence had low comprehensibility in that the listener experienced a
great deal of difficulty during the decoding process.

Dialects of the same language need not be mutually intelligible. In fact, the
distinction between the terms dialect and language is more a political distinction than a

linguistic one based on mutual intelligibility (F asold, 1999). While linguists accept this

16

point (Linguistic Society of America, 1997), members of the general population in the
US. may not. Many people, who speak English in the US, tend to believe that fellow
speakers of English should be intelligible to them. As previously mentioned, English
speakers in the US. will often comment on the language of speakers whom they find
unintelligible or incomprehensible. In the Niedzielski and Preston (2003) study. one
informant contrasted the common belief that dialects of English should be mutually
intelligible with the observation that many of them may not be at times:

And sometimes you really don’t [understand another dialect]. I told your wife

earlier that you CAN understand people in various parts of the United States,

because we all do speak English. but that’s not true. Because I remember
listening to a man in South Carolina, and I really did not understand one word he

said. (p. 110)

Another informant stated his belief that newscasters are taught to speak “Midwestern”
speech because it is more universally intelligible.

The concept of relative inter-dialectal intelligibility was further validated by a
Preston (1997) and summarized in Niedzielski and Preston (2003). Respondents rated
speech patterns found in areas of the US. based upon the perceived “degree of
difference” from their own speech patterns ( 1: no difference, 2: slightly different, 3:
different, and 4= unintelligibly different). Michigan respondents rated Louisiana,
Mississippi, and Alabama speech patterns as unintelligibly different; Indiana respondents
rated Massachusetts speech patterns as unintelligibly different; and Southem respondents
rated Wisconsin, New York, and all of New England speech patterns as unintelligibly
different. Therefore, the concept that dialects within the US. are not always mutually

intelligible appears to be readily accepted among non—linguists.

During the aforementioned internet search. the terms “thick accent” and “thick

17

dialect” were paired with the phrases “hard to understand” or “difficult to understand”
using the same search engine (Google, 2005). The resulting websites predominantly
included discussion about literature written in regional dialects that were difficult to
comprehend for the reader and criticisms of university professors who speak English as a
second language.

The latter case has been validated in several studies of second language
acquisition. It is generally accepted that foreign-accented speech is not as intelligible to
native English speakers as native-accented speech. For example, in an earlier study by
Lane (1963). a list of words was read by native speakers of English. Serbo-Croatian.
“Indian” (sic), and Japanese speakers and presented to native English speakers. The
foreign accented speakers were an average of 36% less intelligible to the listeners than
the speech of native English speakers.

However. a language difference sometimes does not affect intelligibility. per se.
but instead, the more sensitive judgment of con-zprehensibility. Reduced
comprehensibility is often correlated with evidence of increased cognitive load. increased
error rates under certain conditions. or increased response time (Munro & Derwing,
1995b). For example. Nabeleck and Donahue (1984) found that the non-native English
speech in their study was just as intelligible to native English speakers as was the native
English speech under normal listening conditions. When reverberation (an echo) was
superimposed on the audio stimuli to compromise the speech signal. the non-native
speech was rated as significantly less intelligible than the native speech. This
observation suggests that there are increased cognitive processing requirements that

accompany listening to non-native speech. Although the native English listener may be

18

successful in understanding non-native English speech under ideal conditions, minimal
alteration of the listening conditions could make a significant difference.

In a more recent study, Munro and Derwing (1995a) discovered that when a
sample was rated less comprehensible by listeners, it coincided with indicators of
processing costs. English sentences were read by ten native speakers of Mandarin and
ten native speakers of English. English speaking listeners judged whether the sentences
were true or false statements. Sentences judged to be less comprehensible by the
listeners were also answered more slowly than those judged to be more comprehensible.
Munro and Derwing’s study validated the subjective notion of comprehensibility with
evidence that such judgments were associated with actual cognitive effort.

Some researchers have used subjective judgments of comprehensibility as a
dependent variable. In giving subjectivejudgments of comprehensibility, participants are
asked to indicate their own personal opinion of how difficult each speaker was to
understand, regardless of whether the message was actually understood or not (Burda et
al., 2003; Derwing & Munro, 1997; Gass & Varonis, 1984; Munro & Derwing, 1995a,
1995b). These studies indicate that subjective judgments of comprehensibility are
reliable measurements of the difficulty in understanding that may or may not be
experienced by the listener.

Comprehensibility and intelligibility are not static. Numerous factors, including
nonlinguistic ones (i.e. preconceived notions about the speaker; Niedzielski, 1999) may
affect comprehensibility. However. two specific factors will be addressed. namely: (a)
phonemic difference and (b) listener familiarity, as discussed next.

Phonemic ditfi'rence. Phonemic difference refers to the possibility that a dialectal

l9

difference can cause the intended meaning of an isolated word to change entirely. This
was illustrated in a study by Plichta and Rakerd (2002). It compared speakers of two
Michigan dialects. one spoken in the Upper Peninsula and the other in the Lower
Peninsula. Participants from both dialect groups perceived computer generated C VC
words. The investigators discovered that when the words were formed with vowels that
fell in different perceptual boundaries of the two dialects — the speakers of the two
dialects perceived acoustically identical signals as different words. The perceptual effect
was minimized when a short carrier phrase was included before the word that was spoken
in either dialect. This phenomenon indicates that a type of cross-dialectal perceptual
confusion could be somewhat neutralized with context. However, it may require
additional top-down processing to resolve the confusion. if the context is ambiguous
(Munro & Derwing, 1995b).

Listenerfamiliaritfiv. The above cited study also illustrates another factor that
influences comprehensibility and intelligibility. Familiarity with a dialect may increase a
listener‘s ability to comprehend a dialect. This point was made in a study by Gass and
Varonis (1984). They observed that second language teachers were more successful
transcribing sentences at the end ofa sample than they were in the beginning. While this
suggests that a small degree of experience can increase intelligibility, another part of this
study and other studies have demonstrated that a great deal of experience with a dialect
can increase its comprehensibility even further. Gass and Varonis (1984) also compared
inexperienced listeners with the second language teachers using the same non-native
speech samples. The authors discovered that the inexperienced listeners made

significantly more errors in sentence transcription than did the second language teachers

with “many years experience” (p.79).

Similar findings were revealed in a study by Labov and Ash (1997). The words
pronounced by speakers in Birmingham. AL were played in isolation, phrases, and
complete sentences to listeners in Birmingham, Chicago, and Philadelphia. Although,
listener accuracy was greater for all groups when the word was in phrases and sentences.
the Birmingham listeners scored consistently higher than the other listener groups. Even
when the Birmingham listeners were incorrect, their guesses were phonetically closer to
the actual intended word than were the other respondents’ guesses.

In another study, Gerken and Deichman (1979) observed that among untrained
test administrators, African Americans made less errors than Anglo-European Americans
during the transcription of responses to test items on the Weschler Intelligence Scale for
Children (WISC), when the children spoke nonmainstream dialects associated with
African American groups — AAE. Although they did not observe that this interaction
significantly affected the children’s overall test scores, the findings indicated that a
racial/dialect mismatch can cause increased errors in tasks requiring speech intelligibility.
Furthermore, this increase of errors appeared to be due to inexperience with the dialect to
which the person was listening.

Nelson and McRoskey (1978) reported that as African American children became
older, they showed a greater ability to comprehend Standard American English (SAE).
The implication is that with SAE experience comprehension increases for AAE-speaking
children. However, the authors did not determine how well SAE speaking children fared
in their perception of AAE after some experience with the dialect. It is likely that they

did not have the experience with AAE that the AAE speaking children had with standard

dialect. Speakers of minority and majority dialects experience their respective home
dialects during interactions with family and friends. However, minority dialect speakers
have the added exposure to mainstream dialects in educational settings as well as other
settings in the community (Skutnabb-Kangas. 2000). The disadvantage of being a
mainstream dialect speaker is that there likely will be minimal exposure to a diversity of
other (non-mainstream) dialects (Baugh, 1983). Therefore, the speaker of the minority
dialect may learn to understand a wider variety of speakers than the speaker of the
mainstream dialect.

The findings of Gass and Varonis (1984) and Plichta and Rakerd (2002) support
the position that intelligibility with unfamiliar dialects may increase slightly within the
first few minutes of exposure under ideal conditions. However. the findings of Gerken
and Deichmann (1979) and Labov and Ash (1997) suggest that speaking a dialect creates
a comprehensibility advantage with that dialect, which surpasses the transient familiarity
gains from a few minutes of exposure during an experiment.

The findings of Nelson and McRoskey (1978) and Nabeleck and Donahue (1984)
suggest that when the listening situation is analogous to the competing distractions
inherent to daily communication. the improvements attributed to familiarity gained by a
few minutes of exposure are greatly reduced and do not compare to the more robust
advantage gained from the native speaking experience. However. detection of these
subtle, yet important, differences in the degree to which a dialect is understood, requires a
fine-grained measurement tool. Therefore, the subjective and gradient measure of
perceived difficulty of comprehensibility may be more sensitive to processing costs than

are the more objective measures of intelligibility that require the listeners simply to

IQ
Ix)

indicate the word that they heard a speaker say.
Statement of the Problem

The research discussed thus far suggests that when listeners communicate with
speakers of dialects to which they have had little conversational exposure, the listeners
tend to perceive these speakers along a continuum of dialect use. The continuum appears
to coincide with judgments about how detectable and comprehensible the dialect is. This
research is relevant to all professionals in positions of power over speakers of stigmatized
language varieties because theirjudgments may be equated with opinions about speakers’
intelligence, education level, or work ethic. However, some professionals may have a
more direct impact than others. especially when those professionals are required to assess
the speaker’s language specifically. Three issues provided the context for this
dissertation’s research: (a) types of listeners, (b) types of speakers, and (c) types of
linguistic features to investigate.
SLPS as Participants

The profession of speech-language pathology is highly relevant to this research.
The American Speech-Language-Hearing Association (ASHA) is the certifying
organization of speech-language pathologists and other communication science and
disorders specialists. It stipulates the scope of practice for speech-language pathologists
and audiologists. This scope of practice includes many activities related to speech,
language, and swallowing assessment and intervention. Among these activities, SLPs are
licensed to do two related but very different things: (a) evaluate. treat. and prevent
disorders of speech and language, and (b) “provide services to modify or enhance

communicative performance (e.g. , accent modification. transgendered voice. care and

to
DJ

improvement of the professional voice. personal/professional communicative
effectiveness)” (ASHA, 2001, p. I-29). SLPs must differentiate the former from the
latter, to ensure that clients seeking accent modification are not labeled as disordered
(ASHA, 1983b). This task may not always be a simple one depending upon how the SLP
defines disorder and whether or not that definition is interpreted within a cultural context.
Van Riper (1978) defined a speech disorder as a speech difference that 1) calls
attention to itself, 2) interferes with communication, or 3) places an emotional burden on
the speaker. This definition is clear. An SLP should identify if the client’s speech meets
the above three criteria. If an SLP finds a client‘s speech difficult to understand or
noticeable (i.e., the first two criteria of the definition), then the client should be identified
as disordered by the SLP. But such a diagnosis becomes less clearly optimal when
assessing speakers of dialects that stand out from the Standard English varieties and/or
are difficult for the SLP to understand. Some nonmainstream dialect speakers may
satisfy all of the criteria of the Van Riper definition. yet are clearly not disordered.
Alternatively. Payne and Taylor (2005,) suggested that SLPs might use this
traditional definition of speech disorder with the important understanding that all
communication must be understood and studied within the context of culture. Within the
context of culture, the three criteria of the Van Riper definition take on new meaning.
The individual to be diagnosed with disordered speech is one whose speech draws
attention to itself or is difficult to understand within the community from which he/she
learned language namely (i.e., the home speech community). This interpretation of the
Van Riper (1978) criteria may seem simple, but it is very important. It may determine

whether or not a client is accurately diagnosed. If a client speaks with a dialect that is

highly detectable. then an SLP, who uses the Payne and Taylor interpretation. is alerted
to modify the preferred assessment procedures to be more culturally relevant—perhaps
by supplementing test scores with family interviews and naturalistic observations. The
SLP also may use such information to provide more informed accent modification
therapy with a philosophy of empowerment and language choice, as delineated by ASHA
(1983b). Alternatively. the SLP. who uses the more traditional interpretation of the
criteria. may use dialect detectability as the very reason why the child should be
diagnosed as disordered. Regardless of the scenario, an understanding of how particular
dialect features increase or decrease the detectability of a dialect is important.

SLPs are of particular interest in the study of how dialect features affect
perceptual judgments for a number of other reasons: (a) They are in a position of power
over speakers of stigmatized dialects; (b) they are now mandated to consider factors
related to practicing with a culturally and linguistically diverse population; (0) their
profession continues to contain evidence of antiquated ideologies regarding language and
culture. ((11) they are likely to conduct assessments with speakers of dialects other than
their own; and (e) their diagnostic judgments are likely to be affected by any highly
detectable dialects and factors that limit comprehensibility.

SLPs as proflssionals with power. SLPs are in a particularly relevant position of
power in which their decisions are focused on the speech-language behaviors of their
clients. Their decisions can influence how language variation is viewed by the client,
communicative partners of the client. and society in general. For example, ifa school
SLP qualifies speakers of a particular dialect for special education under the diagnosis of

speech-language disordered. then the Folk Theory of Language is validated for anyone

who interacts with those children (including teachers. parents. and peers). In this
scenario, a person perceived to be a "language expert“ labeled the child "disordered”
based upon the normal language features of the child‘s dialect. In other words. regardless
of how the SLP addresses the language difference in therapy, the child has been labeled
disordered as far as the teachers and parents are concerned. Furthermore the diagnosis
was made by a person who should know what disordered speech is. Therefore.
acceptable features of a child‘s dialect will be viewed as “errors” and corrected as such
by teachers.

The misdiagnosis ofdialects as disorders has broader implications than the ones
described thus far. “Languages are today being killed and linguistic diversity is
disappearing at a much faster pace than ever before in human history...” (Skutnabb-
Kangas, 2000, p. ix). Skuttnab-Kangas asserted that the policies and procedures of public
schools aromid the world are largely responsible for this “linguistic genocide” (2000, p.
569). SLPs are central to this discussion. If SLPs, as language experts, continue to label
dialects as disorders. then those dialects will be increasingly perceived as illegitimate
fomis of communication. They will be gradually devalued and eradicated. Since
language is an intrinsic part of culture (Stockman. Boult, & Robinson, 2004'). the cultural
groups associated with these stigmatized languages and dialects will become increasingly
devalued by the population with social and cultural power. Cultures will be increasingly
eradicated along with the language or dialect (Skutnabb-Kangas, 2000). Therefore. SLPs
as professionals in positions in power are especially relevant to the study ofdialect
perception. Their role in the language human rights movement, although locally focused

on one student at a time. is globally relevant. The need for SLPs to be knowledgeable

and accurate when diagnosing disordered speech affects not only the individual client and
his/her family but also the way that other speakers of that dialect are viewed in the
broader community.

Understanding the way that particular pronunciation patterns cause dialects to be
noticed and/or judged as difficult to understand is central to understanding the
misdiagnosis of nonmainstream dialect speakers. The ability to even detect a dialect
must be studied. Additionally. the dialect features that cue an SLP to worry about the
client‘s comprehensibility in the general population must be examined because reduced
speech comprehensibility may be used as a criterion for diagnosing speech disorders.

Professional mandates pertaining to linguistic diversity. The field of
communication disorders has not always attended to issues of cultural and linguistic
diversity. Fortunately, in the past 30 years. the field has undergone a paradigm shift.
SLPs and audiologists are now required to consider issues related to treating and
evaluating an increasingly diverse population (ASHA. 1983b). This multicultural
movement was fueled by a combination of social. political, legal. and practical forces in
the US. These forces converged to create an increasing need for SLPs to become aware
of the effects of cultural and linguistic diversity on the clinical practice of speech-
language pathology. To meet this need, ASHA has mandated two requirements for the
treatment of cultural/linguistic diversity.

First, ASHA in 1983 recommended that SLPs and audiologists consider normal
language variation when making diagnostic and treatment decisions. In their “Position
Paper on Social Dialects,“ ASHA stated that SLPs and audiologists may not label clients

as disordered when the speech or language difference is attributed to nomial language

variation. Such variation includes speaking a native language dialect or speaking English
as a second language (ASHA. 1983b). In the paper. ASHA also stated that SLPs may
engage in accent modification services. which reduce the detectability or increase the
comprehensibility of a client’s dialect or accent to the general population. This service
must be client-initiated. and the SLP must have specific knowledge of the language
variety that a client seeks to modify. A related article addressed some of the implications
for the new position on social dialects (ASHA, 1983a). In this paper the author(s) stated
that “dialect usage exists on a continuum. The number and type of features that have a
high frequency of occurrence may vary from speaker to speaker” (ASHA. 1983a. p. 27).
This statement acknowledges the very premise on which this dissertation research is
based.

Sixteen years after the Position Paper on Social Dialects (ASHA. 1983b) was
published. the second mandate was imposed. ASHA required that information related to
cultural and linguistic diversity be infused into the educational requirements of speech-
language pathology and audiology students (ASHA. 1999). The mandate was intended to
ensure that future SLPs and audiologists receive an education that recognizes their role as
professionals in an increasingly cultural]y/linguistically diverse society. Specifically. it
was meant to ensure an academic and clinical education for clinicians that did not
intrinsically devalue cultures and language varieties that were not considered mainstream
or standard.

Education in the speech-language pathology and audiology professions should be
based on the research in the field. In the context of the mandated infusion of multicultural

information into the communication sciences and disorders curricula. it is surprising that

so little is known about what speech patterns may contribute to an SLP's ability to detect
particular dialects or influence an SLP’s perception of the speaker‘s overall
comprehensibility. This kind of infonnation is needed to provide prospective SLPs with
an education that allows them to serve a wide variety ofclients.

Antiquated paradigms concerning language and culture. Throughout the
multicultural movement in communication sciences and disorders. an increasing amount
of research. especially in the profession of speech-language pathology. has been relevant
to the treatment and evaluation of culturally and linguistically diverse populations. This
research has focused on three areas: (a) analyses of typical and atypical patterns of
speech-language development across languages and dialects (e. g. Anderson. 2002; Brice.
2002; Craig & Washington. 2002; Craig & Washington. 2004; Craig. Washington. &
Thompson-Porter. 1998b; Gutierrez-Clellen. C alderon. & Weismer. 2004; Junker &
Stockman. 2002; Klee, Stokes. Wong. F letcher. & Gavin. 2004; Leonard. 1989. 1999;
Oetting & McDonald. 2001; Seymour & Ralabate. I985; Seymour & Seymour. 1981;
Stockman. 1986. 1996; Stockman & Vaughn-Cooke. 1991; Taylor. 1999; Washington &
Craig. 1994. 1998; Washington et al., 1998; Wong. Leonard. Fletcher. & Stokes. 2004,).
(b) the appropriateness of clinical procedures for cross-dialectal and/or cross-cultural
speech-language assessments (e. g. Anderson. 2002; C heng. 1987; Hemingway.
Montague. & Bradley. 1981; Johnston & Wong. 2002; Pena. Iglesias. & Licz. 2001; Qi.
Kaiser. Milan. Yzquierdo. & Hancock. 2003; Schraeder. Quinn. Stockman. & Miller,
1999; Seymour & Pearson. 2004; Seymour. Roeper. & deVilliers. 2003; Stockman. 1997.
2000; Stockman & Schraeder. 1999; Washington & Craig. 1992; Westby. 1990). and. (c)

to a much lesser extent. the patterns and etiology of diseases and disorders across

different racial/ethnic groups (for a discussion and summary. see Battle. 2002). As is
evident from the large body of research that has emerged from the multicultural
movement. much progress has been made. Yet. the work is not finished.

There continues to be remnants of past ideology inherent in current research and
clinical practices in the field. Research in every scientific discipline. communication
sciences and disorders included. is inherently based on certain socio-political constructs
(Stockman. 1997b). Such evidence may be found in the paradigms used for categorizing
research in the field of communication sciences and disorders. Studies that address
minority groups are often classified as multicultural. while studies that address majority
cultural groups remain under the category of general speech-language research. This
paradigm reflects the notion that there is a mainstream. “culture-free” (and accent/dialect-
free) group of people that requires no specific identity (Stockman et al., 2004). Such a
paradigm may have justification. For example. due to the void of research with minority
groups prior to the multicultural movement. these groups may be understandably granted
unique classification. Such classification acknowledges the timely attention that is being
paid to such groups.

However. regardless of the justification. the effect is the same. Ethnic/racial
minority groups are marginalized as different from the generic culturally-invisible group.
and the majority cultural group is not studied as a distinct. cultural entity. As a result. the
majority. non—recognized cultural group has not been examined as a unique factor in the
misdiagnosis of speech-language disorders in socially stigmatized groups. This is
unfortunate. since members of the majority culture are frequently the gatekeepers for

domains of cultural capital in the US. Their role as gatekeepers has been largely

 

unexamined in the speech-language pathology multicultural literature.

Many professionals in communication sciences and disorders consider themselves
as part of the mainstream or majorin group. According to ASHA‘s 2005 census. only
7% of its members certified as SLPs identified themselves as a racial/ethnic minority.
considerably less than approximately 25% of the US. population. The role of SLPs.
whether members of a minority group or not. is very important in the study of
multicultural issues in the field. They should not be excluded as a culture-free group
tmworthy of specific mention. Their cultural beliefs and behaviors affect the extent to
which stigmatized groups may access domains of social capital. As previously discussed.
they are professionals with a great degree of influence on the way that language variation
is perceived by the community as a whole. Therefore. they should be considered central
to the study of multicultural issues in the field. particularly the study of inter-dialectal
perception.

Do most SLPs adhere to the Folk Theory of Language or the Linguistic Theory of
Language? Do they sometimes invoke one. sometimes another? Little is known about
the predominant paradigm used by SLPs when considering linguistic diversity. Preston
and Robinson (2005) entertained the possibility that some SLPs could operate in
accordance with the Folk Theory of Language. If such a possibility is manifested. normal
language variation that includes features outside the parameters of good language could
be judged disordered speech.

However. it is possible that SLPs are not among the non-linguist general
population group. and do not adhere to such a theory of language. In fact. many SLPs

were required to pass an actual post-secondary course in linguistics during their academic

31

training. Furthermore. recent graduates were required to receive information on treating
culturally and linguistically diverse populations (ASHA. I999). The required curricula of
SLPs. at least those who graduated in the last six years. seems to indicate that SLPs may
receive the information necessary to form a more enlightened and accurate view of
linguistic diversity.

Levy‘s (2004) survey examined the knowledge of undergraduate and graduate
speech-language pathology students. who were supposed to receive an infusion of
multicultural/multilingual issues into their coursework. The survey showed that the
students demonstrated fair to poor accuracy when asked test questions about the
acceptability of various linguistic forms cross-dialectally. This outcome suggests that the
six or more years of training that most SLPs receive may be inadequate to overcome the
years of prescriptive language education and dialectal oppression that they likely received
in their earlier education (Skutnabb-Kangas. 2000). This is likely the case even with the
mandated infusion of rnulticultural/multilingual information into the SLP curricula of the
most recent graduates. The goal of cross-dialectal acceptance may be all the more
difficult if the judgments are more attitudinally constructed than factually constructed.

Perhaps there are more changes that need to be made in the clinical and academic
preparation of SLPs. In spite of curricular changes. informational texts in the field may
maintain past inaccuracies. For example. the most recent edition of T erminologv of
Communication Disorders (Nicolosi et al., 2003'). a popular dictionary for the profession.
shows that the term dialect is defined as a language variety “spoken in a given
geographical area. differing sufficiently from the official standard of the larger language

community" (p. 80) This definition is contrary to the Linguistic Theory of Language that

states that everyone speaks a dialect. including those. who speak whatever is meant by
“the official standard.” It appears to reflect the Folk Theory of Language. which claims
that there is a dialect—free standard variety. The definition is also. of course. factually
incorrect. There is to date no “official" language in the United States. and even those
states which have made English their official language do not officially recognize one
variety as “standard” (Dale & Gurevitz, 1995).

In summary. it is likely that many SLPs base their practice on the Folk Theory of
Language rather than the Linguistic Theory. In such case. the study of SLPs is important
as a factor in the misdiagnosis of minority dialect speakers. However. in determining
perceptions of dialect detectability and comprehensibility. it is not necessary to determine
the conceptual paradigm used by particular SLPs under investigation. These judgments
are merely the foundations on which both accurate and inaccurate diagnoses are based.
In other words. regardless of whether the speaker is judged to be disordered and
incorrect or litigz.ii.stic'c‘tlly different and legitimate. such judgments may be largely
dependent on perceptions of the speaker’s comprehensibility and dialect detectability.

SLPs and cross-dialectal assessnrents. Dialect mismatches between SLPS and
their clients are likely to occur on an increasingly frequent basis. Although the US.
population has been diversifying for many years, SLPs remain a comparatively
homogeneous group (ASHA. 2005). ASHA has identified the racial/ethnic mismatch
between the demographics of its professionals and the overall US. population as a
problem to be targeted in a focus initiative (ASHA. 1995). To address the problem. the
organization has recommended that efforts be made to recruit and retain professionals

that are members of minority groups. However. until more minorities enter the

33

profession. the current demographics ofcertified SLPs are in contrast to those of the US.
This contrast between the demographics of the profession and those of the US. is causing
an increasing number of SLPs to evaluate and treat clients with accents and dialects.
which may be unfamiliar. This discrepancy creates an increasing likelihood for
inaccurate assessments. This problem is particularly daunting considering the inadequate
amount of research that has been conducted on the ability of SLPs to make adequate
diagnostic judgments about speakers of minority dialects and accents. The procedures
that may be most affected by the SLP’s lack of familiarity with a dialect or accent are
those involving subjective measures of the client‘s behavior. such as intelligibility
judgments and other global speech-language judgments.

The abundant research exposing the inadequacy of many standardized tests with
culturally and linguistically diverse populations has caused disillusionment with them.
(Wilson. Wilson. & C oleman. 2000). Therefore. it has been recommended that SLPs use
“alternative assessment procedures" as the “best method for overcoming the inherent
problems in using standardized tests” (Wyatt. 2002. p. 426). These assessment
procedures include analysis of language samples collected in naturalistic contexts and the
use of criterion—referenced as opposed to norm-referenced procedures. The alternatives
to standardized testing may be just as inadequate. if SLPs lack adequate knowledge about
typical language development in minority populations. In such a scenario. the SLP
operates without normative data from standardized tests and lacks information regarding
typical language patterns of diverse groups. Interpretation of these more subjective
alternative assessments must then be based on the SLP’s own personal experiences. If

SLPs experience primarily standard language varieties. then their judgments are likely to

34

be derived from just those experiences. In this way. subjectivejudgments may inherit the
very inaccuracies associated with standardized tests based on normative data primarily
from the majority language community.

The same factors pertaining to the general population’s perception of non-
mainstream dialects should be investigated as possible factors that could compromise the
accuracy of speech-language assessments. In other words. factors pertaining to the SLP.
in addition to those related to the client. should be investigated: 1) comprehensibility of
non-mainstream dialects. and 2) detectability of non-rnainstrearn dialects.

SLPs and dialect comprehensibilitv judgments. When assessing their clients.
SLPs often depend on measures of intelligibility. As a procedure unto itself. the
assessment of intelligibility has been identified as a crucial component of speech-
language evaluations (Kent. Miolo. & Bloedel. 1994). However. a wide range of
diagnostic procedures depend on the speech perception abilities of the SLP. not just the
assessment of speech intelligibility. Reduced speech intelligibility or comprehensibility
may affect any assessment procedure that requires an SLP to make perceptual judgments.
inclusive of standardized test administration. phonetic transcription. language sample
transcription. data gathering during treatment. etc.

For example. Schmid and Yeni-Komshian (1999) examined a task that SLPs
typically perform on a daily basis with their clients — the identification of
mispronunciations. They reported that when Standard American English-speaking
undergraduate SLP and audiology students listened to dialects and accents that differed
from their own. increased cognitive load was indicated by prolonged response time and

increased inaccuracy in identifying when a client had mispronounced a word. They

attributed this increased cognitive load to a reduction in the SLP’s ability to comprehend
non-native speech.

Related to the SLP's judgments about intelligibility is the judgment of how
comprehensible a client will be to the general population. In a clinical scenario. the SLP
will do a phonological analysis that compares a client‘s production to an intended target.
After completing such an analysis. the SLP will decide whether a client‘s speech needs
treatment or not. If so. then a treatment plan is constructed. Such a plan specifies which
features are to be targeted first. Several guidelines have been provided for this decision.
Some guidelines require the SLP to first target those patterns that will most negatively
impact speech intelligibility (Hodson. 2004). Using Hodson’s criteria. patterns involving
consonant omissions are assumed to negatively impact intelligibility the most. Hodson
suggests further that omission ofa post-vocalic final consonant (i.e.. final consonant
deletion) will have a greater impact on intelligibility than omission ofa consonant that is
in a final cluster (i.e.. final cluster reduction). She offered the following schematic of this

intelligibility-process relationship (Figure 3).

Figure 3
Phonological Severity Continuum From the Hodson Assessment of Phonological

Process-3 (Hodson, 2004)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. Acceptable
Profound Severe Moderate Mlld Variations
Extensive Some Some Sibilant Utterance-
omissions; omissions; deviations distortions; final
some extensive from Mild minimal devoicing;
substitutions substitutions and from shifts of regional/
Severe articulation cultural/
categories dialectal
variations

 

 

 

 

 

 

Dialectal variations were included in the category of“acceptable variations"
apparently at the subtle extreme of the continuum. However. some dialects may use
features that are not as subtle as this schematic suggests. Dialects may contain features
described under the categories of Mild. Moderate. and Severe. Little is known about how
SLPs might judge speakers of dialects that include phonemic substitutions or even
deletions. but two outcomes are possible. First, SLPs couldjudge even those more
salient patterns as “acceptable variations.“ with the understanding that they are normal
language variations. The speakers using such phonological patterns would not be
categorized along the continuum of disorder and may or may not be viewed as less
intelligible to the general population. Second. the SLPs could judge them with the same
criteria that one used to judge a phonological disorder. That is. the SLPs will judge

omission patterns as less intelligible to the general population than the speaker who does

37

not use such patterns.

SLPs and dialect c‘letectability judgments. SLPs must become aware of the
features that draw attention to dialects. Such awareness may help them to analyze their
client’s speech more accurately. For example. if the SLP knows that final consonant
deletion is a highly detectable dialect feature that contributes to decreased
comprehensibility. then lie/she can re-evaluate the diagnostic evidence to ensure that the
pattern did not create the illusion of a disorder. Johansson (.1978) alluded to this concept
in the introduction to his study of native Swedish-speakers of English as a second
language when he quoted Robinson (1973. p. 192):

Research into error analysis is vital. since the teacher should know in advance the

type and importance of the error a particular group of pupils would be likely to

make... It may happen that the teacher becomes obsessed with insignificant errors
to the exclusion ofothers that are much more important. Guidelines are required
to help establish a scale of relevant priorities in error correction.

This quotation is equally relevant to the SLP who engages in accent modification
therapy. Albeit a slight change in wording from “error“ to “feature” is required due to
the fact that dialects are systematic and legitimate in their own right (Linguistic Society
of America. 1997; Wolfram & Schilling-Estes. 1998). It also is relevant to traditional
clinical assessments and therapies in which SLPs engage. SLPs are expected not to fault
clients for normal language variation. In this scenario. the “insignificant errors" to which
Robinson (1973) referred are analogous to the normal dialectal variants. especially the
highly detectable features. that could be misclassified as developmental speech errors.

Misclassification is just one possible effect of highly detectable dialect features.

Such features can also alert the SLP that a client speaks a nonmainstream dialect. In such

a scenario. the SLP should become cautious about interpreting standardized test scores.

AAE as a Language Iz'ariety

As discussed thus far. much of the research on the perception of non-mainstream
English dialects has pertained to English as a second language accents. However. this
dissertation focused on first—language dialects. ASHA’s Position Paper on Social
Dialects. stated: “A bilingual speaker may present a situation that is analogous to a
speaker who uses a social dialect” (ASHA. l983a. p. 26). Therefore. much of the
research on perception of second language accents may apply to the study of first
language dialects. But first language dialects may be more prone to assessment
inaccuracies than are second-language accents. given the Folk Theory of Language
(N iedzielski & Preston. 2003).

As previously discussed. The Language is the only legitimate variety of speech in
the Folk Theory. Minority dialect features are considered equivalent to slips-of—the-
tongue and other speech mistakes. A non-mainstream dialect is thought to result from
lack of education. lack of intelligence. lack of effort. or even recalicitrance (suggesting
that nonmainstream speakers actually have the ability to use mainstream varieties if they
simply choose to do so). Second language accents have a very obvious justification—the
speakers spoke a different language as their native language. For the SLP who operates
under the Folk Theory. this means that the second language features used by his/her
client are legitimized. The deviations from Good English are the result of the incomplete
learning of English. The SLP is free to assume that the client probably speaks an
idealized form of his/her native language. and therefore may not be uneducated or
unintelligent. Therefore. the speech patterns of second language dialects may have a

higher status in the minds of non-linguists and possibly SLPs than the features of

39

nonmainstream dialects.

Although many dialects are spoken in the US. the AAE dialect is worthy of
particular study for two reasons: (a) its speakers have been documented to vary in the
number and type of features used. and (b) its speakers are frequently misdiagnosed as
disordered by SLPs.

Description ofAAE. AAE is the language variety spoken largely by African
Americans in the US. It has vocabulary. phonology. syntax. morphology. and
pragmatics that are distinct from Standard American English (Rickford & Rickford.

2000). Historically. AAE may be related to creole languages that evolved from a pidgin
system that combined Western European languages with West African languages
(Mufwene. 1998). It has also. however. acquired features as a result of the early second-
language learning experience. contact with a variety of English dialect speakers. and. of
course. changes and innovations in the variety initiated long after slavery (Wolfram and
Schilling-Estes 1998). The linguistic features associated with the dialect have been
identified in multiple sources (Bailey & Thomas. 1998; Craig et al., 2003; Craig &
Washington. 2004; F asold. 1981; Luelsdorff, 1975; Oetting & McDonald. 2001; Pollock
et al., 1998; Smitherman. 1977. 1994; Stockman. 1986. 1996). Speakers of the dialect

differ in the number and types of features used (ASHA. l983a; Stockman. 2004; Terrell
& J ackson. 2002). Factors such as education level and socio-economic status have been
discussed in several papers (Craig et al., 2003; Craig & W ashington, 2002; Craig &
Washington. 2004; Oetting & McDonald. 2001. 2002; Stockman. 2004; Washington &

Craig, 1994, 1998).

The misdiagnosis ofAAE as disordered language. African American children are

40

over-represented on speech-language pathology caseloads across the country (US.
Department of Special Education Programs. 2004). The main reason for this disparity is
the use of assessment procedures that are highly biased against AAE (Seymour & Bland.
1991). Most SLPs who are Anglo-European American speakers of a mainstream English
variety are unfamiliar with AAE. Consequently. they may judge the speakers unfairly
based on their own beliefs about how understandable the dialect is. Speakers whose
variety of AAE is either very noticeable or not so comprehensible to the SLP may be
misdiagnosed more often than other speakers of AAE. Given this possibility. it is
important to determine how the varying use of AAE features influence the perceptual
judgments of SLPs.

AAE phonology. AAE has a systematic phonological system that differs
significantly from other fomis of American English (Bailey & Thomas. 1998; Luelsdorff.
1975; Pollock et al.. 1998; Stockman. 1996. 2004). Common AAE phonological patterns
have been described in several sources (Bailey & Thomas. 1998; Pollock et al.. 1998;
Stockman. 1996); however. speakers of AAE are diverse in the frequency and types of
phonological features they might use (Bailey & Thomas. 1998; Pollock et al.. 1998;
Stockman. 2004; Wolfram. 1986). For example. one speaker of AAE might use multiple
phonological features commonly associated with the dialect in every sentence spoken.
while another one may use relatively few features. As stated previously. this
quantification of AAE features produced by a speaker is termed dialect density or dialect
rate (Craig & Washington. 1998). Different speakers also may use different types of
features. For instance. one speaker might substitute /v/ or /f/ for final voiced/voiceless -th

sounds. while another one substitutes /d/ or /t/. Yet another speaker may not substitute

41

any of these sounds for voiced/voiceless —th but still use other phonological patterns
associated with AAE. F urthermore. a single speaker may use any. all. or even none of
these possibilities across a variety ofdifferent contexts and still be considered a speaker
of AAE because of other morphosyntactic, semantic. pragmatic. or intonational patterns.

Although AAE's phonology has been studied less than its morpho-syntax (Bailey
& Thomas. 1998). the phonology may have the greater impact on the dialect’s perception.
It has been long known that phonological features of nonnative accents carry more
perceptual weight than morpho-syntactic features and could trigger greater disturbances
in intelligibility (J ohansson. 1978). One possible reason for the perceptual impact of
phonological features is that they occur more frequently than morpho-syntactic ones.
That is. there are more phonemes in any unit of speech than there are morphernes.
Stockman, Guillory. Newkirk. and Seibert (in submission) discovered that AAE
phonological features accounted for a larger proportion of the dialectal variance in the
speech of African American children than did morpho-syntactic ones. The effect of
phonological features may occur so frequently that they contribute to a gestalt perception
of dialect rather than specific awareness of features. Whereas. morpho-syntactic features
may be more categorically salient.

During development. AAE phonology tends to be retained in the classroom past
the age when standard grammatical patterns may be used (Craig et al.. 2003; Craig &
Washington. 2004; Stockman. 1996). A possible reason for the longevity of AAE
phonological features in children could be that the individual phonological features of
their dialect are apt to operate below the level of consciousness. while morpho-syntactic

features may be more obvious to them (Wolfram & Schilling-Estes.l 998). Thus. to code

switch toward a standard dialect. the specific morpho-syntactic features will be targeted
by the speaker first. The phonology may still contribute to the overall perception of
dialect. yet the speaker may remain unaware of what or how to sound more “standard.”
Therefore. when learning to code-switch into a standard dialect. the children may retain
AAE phonological patterns. even when doing their best approximation of the standard
dialect. Although they may not be readily noticed and changed by speakers.
phonological features associated with any stigmatized dialect still can be just as socially
stigrnatizing as morpho-syntactic features. Wolfram and Schilling-Estes (1998) suggest
that consonantal features are especially prone to social stigma compared to features
affecting vowel sounds.

While AAE phonological features can occur in any word position. this
dissertation’s research focused on just those sounds in the final position of words. This is
the position where the majority of AAE phonological features occur (Bailey & Thomas.
1998; Luelsdorff, 1975; Pollock et al.. 1998; Stockman. 1996. 2004). Phonological
features of AAE may cluster at the ends of words because word and syllable final
consonants (i.e.. codas) are more marked than are consonants in word and syllable initial
positions (i.e.. onsets). Languages and dialects are prone to modify final consonants to
create a less marked form (Steriade. 2004). This expectation was validated in AAE when
Craig and colleagues (2003) examined the occurrence of nine different types of AAE
phonological features among children in grades 2 through 5. Three of the four patterns
used most often involved final consonants: (a) Substitutions of th (“with” /w10/ 9 [wlt]
or [wlt]) (b) consonant cluster reduction (e.g.. “fast” /faest/ -) [faes]). and (c) postvocalic

consonant reduction (“feet” /fit/ 9 [fi]).

43

F requencv and Type ofFeatures as Variables

The research reviewed thus far suggests that several variables should be
considered in studies that aim to determine the impact of dialect features on cross-
dialectal perception. The study by Johansson (1978) and the testimonials from non-
linguists (Alvarez & Kolker. I986; Niedzielski & Preston. 2003) suggest that the two
judgments of dialect detectability and dialect comprehensibility are direct indicators of
dialect density perception. Listener judgments may be affected by numerous factors. two
of which are related to the dialect patterns used in a given unit of perceived speech: (a)
number of dialect features present. and (b) type of dialect features present.

Frequency offl'atures included. If a speaker uses more dialect features in a given
unit of spoken language. then it is logical to assume that the presence of a particular
dialect would be detected more quickly and easily. For example. Ryan. Carranza. and
Moffie (1977) revealed that the degree of perceived foreign accent was positively
correlated with the number of segmental substitutions in a brief reading passage. This
outcome offers perceptual justification for the use various methods for calculating dialect
density within the child language acquisition and disorders literature. as summarized by
Oetting & McDonald (2002').

These meters of variable use of recognized dialect patterns commonly reflect an
average of the number of features used per some linguistic unit. The term dialect density
appears to be analogous to the concept of physical density (mass per unit of volume). In
physical density. each gram representing a unit of mass is equal to every other gram.
Thus. increases in density are invariably linear on a ratio scale (with an absolute zero). A

. . . . t
substance measurrng Sg/cm3 IS five times more dense than a substance measuring lg/cm”.

44

This may be where the similarities between dialect density measurement and physical
density measurement end. Dialect features may not affect perceptual judgments equally.
That is, a phrase containing five phonological dialect features per unit of language may or
may not be five times more perceptually salient than one containing one dialect feature
for the same unit of language. Perceptualsalience may depend on what type of dialect
features are included in the sentence. Also. each additional feature may affect perception
in differing ways. It is possible that there is a threshold and/or a plateau for the
perception of dialect features. A threshold means that a certain number of features per
unit of language would be needed to even detect that the dialect is being spoken. A
plateau means that the inclusion of a certain number of features may cause the perceptual
judgments to level off. creating little change when additional features are added.

Types of features included. Research in the field of perceptual dialectology
suggests that the degree to which a dialect is noticed camrot be explained fully by the
sheer number of dialect features present in a unit of language. In some instances. a single
linguistic cue carries so much perceptual weight that it can override the perception of
other dialect features in the same context. For example. Graff. Labov. and Harris ( 1983)
reported that when the onset of the diphthong /aU/ was acoustically altered to become
[an] in the speech of an African American. Philadelphia judges identified the speaker as
Anglo-European American —— despite the presence of AAE phonological features
included throughout the rest of the sentence.

J ohansson ( 1978) hypothesized that all linguistic features were not perceived
equally. He studied the perceptibility of the various speech errors made by native.

Swedish-speakers. who were learning to speak English. A fomrula was developed for the

45

perceptually-based evaluation of non-native error patterns. The formula involved: (a)
determining whether an error caused increased difficulty for the native listener to
understand the message of the non-native speaker. and (b) determining the degree to
which the error was “irritating” to the listener. These two components are strikingly
similar to the two components of intelligibility/comprehensibility and detectability used
by the non-linguists in the testimonials previously described in this chapter (Alvarez &
Kolker. 1986; Niedzielski & Preston. 2003). An error that is comprehensible but is not
irritating would constitute the lowest grade oferror. In the context ofthis dissertation.
this interpretation can be rephrased as follows: a dialect feature that does not reduce
understanding and is not very detectabile is less likely to impact the perceptual judgments
of SLPs than other features. Johannson concluded that: (a) phonological errors carried
more weight than morpho-syntactic errors. (b) phonemic errors were judged as more
serious than subphonemic errors. and (c) subphonernic errors that differed greatly from
the target were judged more severely than were those that did not differ as much from the
target.

F lege and Hammond (1982) also focused on the relative salience of various
phonological patterns in non-native speech. Using a delayed mimicry paradigm. they
asked native English speakers enrolled in first-year Spanish classes to read sentences in a
“typical Spanish accent.” The type of sound substitutions used by the students was
analyzed. The students used several sound substitutions that are typically heard in
Spanish accented English. but some were used more than others. The relative ranking of
the sound substitutions used suggested that some substitutions were more salient than

others. However. the delayed mimicry paradigm is based on several assumptions. These

46

assumptions pertained to the folk (i.e.. non-linguist) awareness oflinguistic variation. as
discussed by Preston (1996a). He asserted that the awareness of dialect features by non-
linguists fell along four dimensions: (a) availability. (b) accuracy. (c) detail. and (d)
control.

Availability concerns the degree to which a given person might attend to linguistic
details. He/she may range from complete oblivion to a preoccupation with a linguistic
event. Accuracy refers to the level at which the person can correctly convey the existence
of dialect features. This dimension ranges from absolute accuracy to complete
inaccuracy. Detail regards the degree to which the person is able to refer to specific
elements of a language variety. This scale ranges from a general awareness of the
linguistic phenomenon to the ability to cite specific details about it. C ontrol concerns the
person’s ability to imitate the dialect. Listeners may have absolute. partial. or no
imitative control over production ofa given dialect. An ability to imitate. even accurately.
for example. might not indicate a higher setting on the other scales of awareness.

To summarize. evidence supports the notion that some linguistic patterns may be
more salient to listeners than are others. While the number of dialect features in a unit of
language may affect the degree to which a dialect is perceived. it does not entirely
explain variable perceptual judgments. Instead. the specific types of features presented
must be considered. Furthermore. a linear. ratio-scaled relationship between the number
of features present and perceptual judgments should not be assumed.

The determination of which features are likely to be detected more than others
may be based on perceptual salience (i.e.. the degree to which listeners can detect a

feature in non-meaningful speech). Recent research on optimality theory of phonology

47

 

suggests that. cross-linguistically, speakers tend to use phonological alternations that are
the least perceptually distant from an intended phonemic prototype. This is done while
trying to obey constraints within the phonological system (Steriade. 2004). In optimality
theory. constraints are ranked according to importance as determined from the language
community (Prince and Smolenski.l993). They involve competing desires for
faithfulness to a prototype and reluctance to use marked features. For instance. if a
speaker has a constraint against final voiced obstruents (a relatively marked feature) and
produces the word head. then lie/she will most likely satisfy this constraint by producing
/bit/. because the /t/ has the least perceptual distance from /d/ (the intended prototype).
when compared to other options (e.g.. consonant deletion).

Steriade‘s (2004) framework for evaluating perceptual distance between
phonological alternations is called the “P-map.” The P-map is an abstract representation
of the perceptual distance between phonetic forms. Steriade discusses how the P-map
includes phonological repairs to satisfy the constraint against final voiced obstruents
common in human languages. Several types of repairs are: epenthesis (inserting a vowel
after the final voiced obstruent; /bid/—> /bidi/). deleting the final consonant altogether
(/bid/—-> /bi/) . or changing the obstruent into a continuant (lbidl—r /biz/). Yet. a fourth
option. devoicing the final consonant (lbid/-+ /bit/)_. was the option used by most of the
.world's languages to satisfy this constraint. Furthermore. Steriade showed that this
outcome was perceptually the least noticeable when compared to other processes to avoid
a final voiced obstruent.

The P-map is a theoretical model. and a complete representation of the relative

perceptual salience of all possible phonological changes in all phonetic environments

48

does not yet exist. Steriade. however. did provide some guidance in determining relative
perceptual salience between phonological patterns. She suggested judgments of
similarity or degree ofconfusability could be used.

Steriade asserted that the phonological patterns used to satisfy phonological
constraints by most of the world‘s languages are the ones that are the least perceptually
salient. This assumption has important implications for the present study. Dialect
features with the greatest perceptual salience are not likely to be used widely by the
speakers of AAE. Therefore. they could either increase the detectability ofthe dialect by
virtue of their perceptual salience or be ignored as random anomalies by virtue of their
rarity in AAE speakers. Conversely. if a feature has low perceptual salience. it may not
be readily detected by listeners. or it may be identified as characteristically iconic of
AAE due to its ubiquitous nature among speakers of the dialect.

It is unknown how these possibilities will reveal themselves in the perception of
dialect detectability. Irvine (2001) alluded to these possibilities in relation to the power
of specific dialect features to trigger attitudes about speakers. He suggested that some
features can immediately register the perception of the dialect. Such features may be
used more frequently by speakers of the dialect and become semiotically linked in the
minds of the listeners. In terms of the perception of dialect detectability. a feature with
low perceptual salience may contribute greatly to ratings ofdialect detectability just
because it is readily identifiable as a frequently used feature of the dialect.

Research Questions
Three factors were considered in the study of perceived dialect density: (a) the

listeners. (b) the speakers. and (c) the dialect patterns used. In this investigation. the

49

 

listeners were Anglo-European American SLPs from predominantly Anglo-European
American school districts. The speakers were native AAE speakers. The sentences
spoken included phonological patterns affecting word-final consonants that vary in both
number and type.

The research was guided by the following general question:

Are Anglo-European American SLPs‘ perceptions of dialect detectability and
comprehensibility influenced by the number and type of AAE phonological features
present within the sentence?

The perception ofdialect detectability and the perception ofcomprehensibility are
two separate issues. that were studied separately. Therefore. the general question
involves the following two specific questions:

(a) Are Anglo-European American SLPs’ perceptions of dialect detectability influenced
by the number and type of AAE phonological features included in the sentence?

(b) Are Anglo-European American SLPs’ perceptions of comprehensihility influenced by
the number and type of AAE phonological features included in the sentence?

While the perception of dialect detectability and comprehensibility will be
analyzed separately. they have similar hypotheses. It was hypothesized that the sentences
with the greater number of features would be judged as having more detectable use of
AAE dialect and reduced perceived comprehensibility. It is also hypothesized that the
inclusion of perceptually salient features will increase dialect detectability and decrease

comprehensibility.

 

CHAPTER 11
METHOD

The experiment was designed to determine the impact of selected AAE
phonological features on perceptions of comprehensibility and dialect detectability by
SLPs in predominantly Anglo—European American public school districts. It examined
the effects that would be attributed to the number of features present. type of features
present. or a combination of both aspects. Two categories of features. less salient and
more salient. were explored. with varied frequencies in sentences. The two categories of
perceptual salience were based on results of a preliminary study to be described briefly in
this section (see Appendix A for a detailed description). The overall aim of the
dissertation‘s main study was to answer the following question: Is the perceived degree
of comprehensibility and dialect detectability influenced by: (a) the number of AAE
features contained in a sentence and (b) their relative perceptual salience grouping?

Participants

Characteristics

The participants in this study consisted of 16 public school SLPs. They
completed a brief questionnaire that probed their conversational experience with AAE.
their experience with treating AAE-speaking clients. their own personal use of AAE. and
their number of years experience as an SLP. Total years ofexperience ranged from 3 to
33 years (M: 11.88. SD = 8.77).

To qualify for participation in the study. the SLPs had to indicate that (a) they had
experienced conversational exposure to AAE less than 5% of the time. (b) they had had

fewer than three clients within the last 3 years that spoke AAE. (0) they. themselves. did

51

 

not speak AAE. and (d) they had worked as an SLP for at least 2 years. Each participant
also passed an air conduction hearing screening at 25db HL for 1000. 2000. and 4000Hz.
The investigator administered the screening using a calibrated Beltone portable
audiometer (model # 119) with bilateral earphones in a quiet school office setting.
Recruitment

The participants were recruited from school districts in Ionia and Eaton counties.
two adjacent rural mid-Michigan counties. The school districts had student populations
that were 90% Anglo-European American and less than 5% African American. as
indicated by the county’s census from the preceding school year 2003 (Amrie E. Casey
Foundation). The investigator attended a regularly scheduled. Ionia county-wide SLP
meeting to recruit participants. During the meeting. the study’s procedures were
described. but the variables and hypotheses remained confidential. A sign-up form was
distributed to collect contact information from those SLPs that were present. Following
the meeting. some SLPs provided contact information for fellow SLPs that were not
present as well as contact information for potentially interested SLPs in adjacent
counties. The SLPs were contacted via telephone. email. and/or in person for scheduling.
They were compensated $15 for their time. In total. 14 SLP participants represented
seven different school districts in Ionia county. and 2 SLPs represented two different
school districts in Eaton county.
Human Subjects Approval

The study was approved by the Michigan State University Internal Review Board
as acceptable with regard to the protection of participant rights and privacy prior to

engaging participants in the experiment (see Appendix C).

General Description of the Stimuli

The independent variables in this study were (a) frequency of AAE features
included. and (b) relative salience of AAE features included. The stimulus set consisted
of 18 different sentence wordings selected to feature various combinations of these two
variables (see Table 1a). Word combinations varied only in the subject-noun and object
of preposition positions. The words of the sentence recordings were produced with
different pronunciation patterns. as follows: Six different sentences had no AAE
phonological features. six had one AAE feature. and six had three AAE features (see
Table la/b). A more detailed description of the sentences and phonological patterns
appears on pages 59-61.

In total. the participants made 42 ratings: 36 ratings for the dialect detectability
task and 6 ratings for the comprehensibility task. The 18 items in Table 1b were
presented twice. During one presentation. they were spoken by one AAE speaker. and
during the other presentation they were spoken by another AAE speaker (both in
randomized orderings). creating a total of 36 stimulus sentences. The participants rated
each of the 36 sentences individually during the dialect detectability task and rated the 36
sentences presented in 6 groups of 6 items each (see Table 2) during the

comprehensibility task.

53

Table 1a
Listing of Stimulus Sentences and Targets for Planned Alternations

Sentences and distribution of phonological processes. Highlighted cells represent
planned phonological features.

Phonological Features Present: F Dk=F inal Deletion of /k/. FDt=Final Deletion of /t/.
FRk=Final Cluster Reduction of sk. FRt=Final Cluster Reduction of st. t/th=Substitution
of [t] for /0/. and f/th=Substitution of [f] for /0/.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percep-
tual Phonolog
Salience i-cal
No. of of Features Sent. Adverb
Features Features Present No. Det Noun Verb Prep. Det Obj -ial
may Mon-
0 More None 1 A sock be with my teeth day
may Mon-
0 More None 2 A cat be with my sock day
may Mon-
0 More None 3 A tooth be with my boat day
may Mon-
0 Less None 4 A desk be with my teeth day
may Mon-
0 Less None 5 A guest be with my desk day
. may Mon-
0 Less None 6 A tooth be with my list day
may Mon-
1 More FDk 1 A sock be with my teeth daL
may Mon-
1 More FDt 2 A cat be with my sock day
may Mon-
1 More t/th 3 A tooth be with my boat day
may Mon-
1 Less FRk 4 A desk be with my teeth day
may Mon-
1 Less FRt 5 A guest be with my desk day
may Mon-
1 Less f/th 6 A tooth be with m list da
FDk, t/tn, may Mon-
3 More t/th 1 A sock be with my teeth day
FDt, t/th, may Mon-
3 More FDk 2 A cat be with my sock day
t/th, t/th, may Mon-
3 More FDt 3 A tooth be with my boat day
FRk, f/th, may Mon-
3 Less f/th 4 A desk be with m teeth day
FRt. f/th, may Mon-
3 Less FRk 5 A guest be with my desk day
f/th, f/th, may Mon-
3 Less FRt 6 A tooth be with my list day

 

54

 

Table lb

Listing of Stimulus Sentences and Targets for Planned Alternations

Phonetically Transcribed
Phonological Features Present: F Dk=F inal Deletion of fk/. F Dt=F inal Deletion of /t/.

FRk=Final Cluster Reduction of sk. FRt=Final Cluster Reduction of st. t/th=Substitution
of [t] for /S/. and f/th=Substitution of [I] for /S/.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percep-
tual Phono-

No. of Salience logical Ad-
Fea- of Features Sent. verbi

tures Features Present No. Det Noun Verb Prep Det Obj al
me mAn-

0 More None 1 3 80k bl W19 maI ӣ19 de
a me WIS maI mm-

0 More None 2 kaet bi SCIk de
3 me WIS maI mAn-

0 More None 3 ӣ119 bi bot de
a me WIS maI mAn-

0 Less None 4 dSSk bi tiS de
a me WIS maI mAn-

0 Less None 5 gSSt bl desk de
3 me WIS maI lIst mAn-

0 Less None 6 we b' de
a me WIS maI mAn-

1 More F Bk 1 SG bi tiS de
8 me WIS maI mAn-

1 More FDt 2 Re bi sak de
a me WIS maI mAn-

1 More t/th 3 tut bi bot de
3 me WIS maI mm-

1 Less FRk 4 dSS bi tiS de
a me WIS maI mm-

1 Less F Rt 5 988 bi desk de
3 me WIS maI lIst mAn-

1 Less f/th 6 tur I" de

FDk. tlth, a me WIS maI mAn- I

3 More t/th 1 sq bi tit de
FDt, tlth, a me WIS maI mAn-

3 More FDk 2 1% bi SCI de
tlth, tlth, 9 me WIS maI mAn-

3 More FDt 3 tut bi b0 de
FRk, f/th, 3 me WIS maI mm-

3 Less f/th 4 des bi t i 1” de
F Rt, f/th, 8 me WIS maI mAn-

3 Less F Rk 5 988 bi des de
flth, flth, a me WIS maI mAn-

3 Less F Rt 6 tuf bi lIs de

 

 

 

 

Table 2

Characteristics of Six Groups of Sentences Presented During Comprehensibility Ratings

 

 

 

 

 

 

 

 

Stimulus Group Salience Frequency
1 Less 0
2 Less 1
3 Less 3
4 More 0
5 More 1
6 More 3

 

 

 

 

 

Note: The 6 groups contained the sentences listed in Tables 1a and 1b organized by
perceptual salience and frequency (number of features).
Construction ofthe Stimuli

Number ofFea/ures Included

The number of dialect features (i.e.. frequency) was represented by three groups
of stimulus sentences: (a) six sentences with no AAE features. (b) six sentences with one
AAE feature. and (c) six sentences with three AAE features. When one feature was
present. it always occurred in a word located in the subject-noun position. When three
features were present. they occurred in words located in the subject-noun, the preposition.
and the prepositional object-noun positions. Words containing AAE features are
represented in the shaded cells in Tables 1a and 1b.

Specific reasons for the three frequencies tested were. as follows: (a) the zero-

feature level was used as a control condition in which all final consonants were

56

pronounced in accordance with a standard dialect. (by) the one-feature level was used to
examine the individual effect of each phonological feature on the dialect detectability
ratings. and (c) the three-feature level was used to provide an extreme condition in which
a sentence contained AAE patterns in all positions of opportunity (i.e.. a condition in
which every opportunity for AAE consonant phonological features was manifested).
Ttpe of Dialect Features Included

Phonological patterns. The types ofdialect features included in the stimulus
sentences consisted of the phonological patterns presented in Table 3. These six specific
phonological patterns represented the three most frequently occurring consonantal
patterns used by African American children in a study by Craig and colleagues (2003):
(a) “th” substitutions. (b) cluster reduction. and (c) final consonant deletion. respectively.
Patterns involving /t/ sounds and /k/ sounds were placed in separate categories because /t/
has been cited within literature as a commonly deleted phoneme in many American
English dialects (Guy. 1980; Stockman. 2006) while /k/ deletion is not as commonly
documented across dialects. The t/th and f/th substitution patterns were also separately
categorized due to the fact that they are distinct "th" substitution patterns variably found

in AAE (Pollock. et al. 1998).

57

Table 3

AAE Phonological Patterns Included in the Stimuli

 

 

 

 

 

 

 

Phonological Pattern Abbreviation Used Example
Final Deletion of /t/ FDt "Cat” /r<®t/ --> /r<(~)/
Final Deletion Of /I(/ FDR “Duck” /d pk] -->

/d (.7/
Final Cluster Reduction FRt "List” /?rlot/ --> / Mo/
of /st/
Final Cluster Reduction FRk “Desk" /580'K/ -->
of /sk/ /680/
Final t/th Substitution t/th “Teeth” ltrT/ --> /‘L't‘t'/
Final f/th Substitution f/th “Teeth” /nT/ --> /n¢/

 

 

 

 

 

Perceptual salience groupings. These six phonological patterns were categorized
into two salience groups. This was done in order to combine different sound patterns in
individual sentences while continuing to have a systematic basis for comparison.
Evidence from Labov (1966) suggested that if the same phonological feature is perceived
multiple times. then a cumulative perceptual effect could result. In terms of Johansson‘s
(1978) model of feature weighting discussed earlier. a phonological feature may become
increasingly “irritating” to the listener if used repeatedly. Such an effect could increase
dialect detectability. Although this was a valid point to consider. it was beyond the scope
of this study. Categorization allowed different features to be used within single
sentences. thus counteracting any effects from a sole feature being experienced
repeatedly in single sentences.

Perceptual salience of the sound patterns in non-symbolic syllables was used to
group the phonological patterns. As discussed previously. evidence from Steriade (2004)

indicated that perceptual salience might be linked to how noticeable a feature would be in

58

running speech and how frequently the same feature would be likely to occur across
languages and dialects. The relative perceptual salience of the phonological patterns
under investigation was determined from a preliminary experiment. The preliminary
experiment is described in detail in Appendix A.

In summary. Anglo-European SLP graduate students were presented pairs of two-
syllable. non-symbolic units (i.e.. two-syllable nonsense words) that simulated the
phonological environment of the variable subject-noun followed by the verb complex
“may be” used in the main study’s stimulil. When the two syllable units in the pairs were
different. the difference simulated the phonetic difference that occurred in the AAE
phonological patterns to be studied. For example. to examine the perceptual salience of
final /t/ deletion. the contrasted syllables were [dat me] and [da me]. The [dat] in the first
syllable of the first unit simulates a subject-noun with a final consonant /t/. The [da] in
the first syllable of the second unit simulates the same word in which the final /t/ was
deleted. The [me] in the second syllable of both units represents the first syllable of the
verb complex “may be” that immediately followed the subject—nouns in the main study.

Participants rated each pair on a 4-point scale (1: exactly the same. 2= almost the
same. 3= significantly different. and 4= extremely different). Some pairs of nonsymbolic
units were identical (e. g.. [dat me] — [dat me]'). These were considered control pairs and

the ratings for these were subtracted from the pairs containing different units (i.e. the

 

1 Two other phonological features were also tested in the preliminary study. postvocalic lr/ deletion (i.e. in
which the vowel sound is not changed: four -) /fo/) and vocalization of /r/ (i.e. in which the final /r/ is
changed to a schwa: four -) /fo<—>/). The results showed that these two sound changes were both highly
salient. However. they did not provide a dichotomy from which differences could be compared in the main
study. Additionally. these two features were the only ones that included vowel changes. Further
examination of AAE features including lr/ would be better examined in another study.

59

experimental pairs) to get a perceptual salience rating for each phonological pattern to be
studied. The perceptual salience averages were ranked for each pattern. The perceptual
salience rankings resulted in the saliency groupings presented in Table 4. See Appendix

A for a more detailed presentation of the preliminary study‘s results.

Table 4

Ranking of Phonological Patterns for Perceptual Salience in Nonsymbolic Units

Salience Grou n Ranki Pattern Mean SD

More 1 F Dk 1.88
More t/th 1.87
More FDt 1.65
Less FCt 1.48
Less FCk 1.33
Less f/th 0.35

 

Note: Means were obtained from the preliminary experiment data by subtracting the

perceptual rating of control pairs from experimental pairs for each phonological pattern.

Description of the Sentences

The stimulus sentences consisted of eight words each (see Table la/b). The
sentences each consisted of the following constituents: determiner + subject noun + verb
complex + preposition + possessive determiner + object noun + final adverbial. For
example. in the less salient group. one sentence was “A cat may be with my sock
Monday.” In the more salient group. one sentence was “A guest may be with my desk
hAondayf’

The sentence constituents met six criteria: (a) The subject detemriner was the

Word a to avoid opportunities for dialectal substitutions of d/A in words such as the. this.

60

or these. (b) Only words that were plamred to have a phonological alternate at some point
during the sample contained codas. or syllable-final consonants (the subject-noun.
preposition. and the prepositional object-noun) were used. In these cases. only the codas
were opportunities for AAE phonological features. All other words had a CV syllable
structure. (c) The codas always preceded the consonant /m/ to control for coarticulatory
effects (McCarthy. 1986). (d) The verb complex included the words “may be” in all of
the sentences. This specific verb complex was chosen because: it allowed a grammatical
construction without a bound morpheme attached to a verb stem. and it began with a /m/.
thereby allowing comparative environments for the preceding coda consonants of the
subject-noun (see criterion 3). (e) The verb complex was followed by the preposition with
because it was a word that could be used as a planned phonological alternation in both the
more and the less salient stimulus groups (e. g.. with-) [wlt] in the more salient group.
and wit/19 [wa] in the less salient group). (it) The preposition with was followed by the
possessive determiner my because it began with a /m/ to satisfy criterion #3. and it was
CV syllable structure (see criterion #2). Monophthongization of the diphong /a1/ is a
feature of AAE; however. the production of “my” was kept consistent across all of the
sentences. Both speakers produced the diphthong /aI/ in all readings without being
instructed to do so.

To summarize the above criteria. the stimulus sentences included both variable
words and fixed words. The fixed words were: (a) the noun phrase determiner a. (b) the
verb may be. (c) the preposition with. (c) the determiner my within a prepositional phrase.
and (5) the adverbial Monday. These words together created the carrier phrase A __

may be with my Monday. as presented in Table 1.

61

' It

The variable words included the (a) subject-noun. and (b) prepositional object-
noun. The preposition with varied only in pronunciation (i.e. /wlS/. /wIt/. and /wa/). The
actual word remained the same in all sentences (see Table 1b. p. 54).

Preparation of the Stimulus Recordings

Speakers’ characteristics. The speakers were two African American adult
females who spoke AAE and had the bidialectal skill to add or take away AAE
phonological features upon cue with practice. The speakers were recruited from a
university theater program and a university communications program. They both signed
a consent form (see Appendix D).

To account for speaker-specific effects. two speakers were used. Although it
would have been desirable to record both a male and a female speaker given that gender
differences could exist. this procedure would not have permitted a gender effect to be
isolated from a speaker effect when using just two speakers. Because gender differences
were beyond the scope of this study. two speakers of the same gender were used.

Speakers" conditions. The speakers were recorded in a quiet room. A Logitech
USB desktop microphone (Model #980186-0403) was placed approximately 6 inches
from each speaker's mouth. The microphone was elevated so that the speakers could sit
in an upright. relaxed position. The speech samples were digitized using the Wavesurfer
software. version 1.8.3 (Sjolander & Beskow. 2004). at a sampling rate of 22.050 Hz.
The waveform on the computer screen was displayed for the speakers. They were told to
watch the waveform and adjust their vocal volume to make the waveform fill the display
window but not “clip off.” The investigator also watched the waveform to monitor the

sound level. Both speakers were asked to re-record at least one sample because of too

 

little vocal volume.

Prior to the recording. each speaker was instructed to consider the context that
would be presented to naturalness raters and the participants of the study (see
Verification of Naturalness on page 69 for a description of the context). They were
encouraged to practice each sentence before recording it. Once a self-deterrnined.
comfortable intonation pattern was produced. the speakers were instructed to read each
sentence again with the same intonation pattern. They were recorded reading the
sentences in this way three times. These productions were referred to as the neutral
readings. The neutral readings were used as the phonetically consistent carrier phrases
into which variable words were inserted. Only the recordings of the lexically and
phonologically consistent words were used from this reading (i.e. the detemriners a and
my. verb complex may be. and final adverbial Monday). The resulting intonation pattern
was similar between the two speakers. It included primary word stress on the subject-
noun and secondary word stress on the object-noun of the preposition (the two variable
word positions). Pitch was raised on these two words. while a lower pitch was used on
the other words in the sentences.

Immediately following the neutral readings. the speakers read the sentences aloud
again using the same intonation pattern. This time. they were instructed to pronounce
each word making sure that the final consonants were consistent with the word spellings
(see Table 1b). These productions were referred to as the SAE readings. The SAE
readings were also recorded three times.

Finally. the speakers read the sentences again and made the specific AAE

phonological changes on the subject-noun. preposition with. and the object-noun of the

63

preposition (see Table 1b). These productions were referred to as the AAE readings.
During this portion of the recording session. both speakers stressed the preposition with
while attempting to make the required phonological modifications. The speakers were
reminded of the intonation used in the neutral readings and then spoke the sentences
without stressing the word with. The AAE readings were also recorded three times.

The variable words from the SAE readings and the AAE readings were embedded
into the appropriate position of the neutral readings. This was done to maintain some
consistency in the carrier phrases across the experimental conditions. It was necessary to
keep the carrier phrases phonetically consistent to control for any unplanned vowel
changes that might occur as a result of the speakers” attempts to produce variable dialect
features during the SAE and AAE recording processes. For example. the sentences
including three planned alternations (AAE readings) could have included vowels that
were more characteristic of AAE than the sentences containing no planned alternations
(neutral and SAE readings). These vowel differences could have affected the perceptual
judgments. as was demonstrated in the study by Graff and colleagues (1983) discussed
earlier. Therefore all of the carrier phrases were obtained from the neutral readings.

Constrzustion ofmttutttlness rating stimulus CDs. In total. 54 sentence recordings
were obtained from each speaker. The six different sentence wordings were repeated
three times in three different styles (neutral. SAE. and AAE). All of the sentence
recordings were spectrographically displayed on a computer monitor using the
Wavesurfer software (Sjolander & Beskow. 2004) that was used during the audio
recording process. The investigator modified the neutral readings from the two speakers

by replacing the acoustic signal that corresponded to the subject noun. preposition with.

64

and the object noun of the preposition with a corresponding acoustic signal in either the
AAE readings or the SAE readings. Table 5 presents a description of the source (i.e.
neutral. SAE. or AAE) from which each constituent of the stimulus sentences was

obtained.

65

Table 5

The Recording Source of Each of the Stimulus Sentence Constituents

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Constituents of Stimulus Sentences
Varia- Fixed Variable Fixed Variable Fixed Variable Fixed
bility
Part of Deter- Subject- Verb Prepo- Deter- Object-noun Adverbial
Speech miner noun complex sition miner
Word A * may be with my * Monday
0 Neutral SAE Neutral SAE Neutral SAE Neutral

(:3 1 Neutral AAE Neutral SAE Neutral SAE Neutral
Gun

6

2", 3 Neutral AAE Neutral AAE Neutral AAE Neutral
.9

E

5

Z

 

Note: Recording sources: Neutral = the initial recording by the speakers in which no

phonological features were dictated. SAE = the second recording by the speakers in

which spelling consistent pronunciation was prescribed. and AAE. = the third recording

by the speakers in which specific AAE phonological patterns were prescribed.

* Lexically variable word. see Table 1a. p. 53. for specifics.

Three lexically and phonologically consistent stimulus sentence recordings were

created for each sentence pronunciation (see Table lb.) and compiled onto a compact disc

as a prototype of the recordings to be used in the naturalness rating C Ds. These

Prototypic recordings were played for a female African American university professor

and researcher of AAE phonological patterns. Both she and the investigator listened to

66

 

the prototypes and determined if they were reasonable representations ofthe AAE
phonological patterns to be studied. The only modification that resulted from this
listening involved Speaker 1. Perhaps because of her desire to pronounce a clear final
consonant in the SAE recordings. Speaker 1’s three productions of final /t/ on the word
cat were pronounced with a long aspiration duration (M = 821115. SD = 921115). This was
considerably longer than the 80th percentile (62.2ms) of 808 conversational /t/ aspiration
durations reported in Greenberg. Hollenback. and Ellis“ (1996) study of phonemic
parameters produced in conversation. It was also considerably longer than the /t/
durations produced from Speaker 2 (M = 44ms. SD = 6.9ms). The duration of Speaker
2’s /t/ aspirations on cat were similar to the /t/ aspiration durations associated with the
50th percentile (3891115) in the study by Greenberg and colleagues. Presumably because
of this uncharacteristically long /t/ duration. the investigator and the second listener
perceived an /s/ rather than a /t/. Therefore. the aspiration duration was shortened to 40
ms by deleting approximately 40 ms from the final portion of the aspiration signal. Upon
review of the recording. a final aspirated /t/ was perceived by both the investigator and
the second listener.

The sets of three complete stimulus sentence recordings were randomized via an
online randomization program (Dallal. 2003) in two different permutations. The sets of
three recordings of each sentence were embedded onto separate slides of a PowerPoint.
version 2002. presentation.

Verifying Naturalness ofStimulus Sentences
Because the sentences were acoustically modified to control for a number of

criteria. it was possible that listeners would perceive some of the recordings as unnatural.

67

Therefore. to ensure that the most natural sounding recordings were used for the study.
the three recordings per speaker ofeach of the stimulus sentences underwent the
following verification process.

Naturalness rater clutracteristics. Six adult AAE speakers (3 men and 3 women).
who were within the age range of 18-35. functioned as natia'alness raters. Three of the
naturalness raters. one male participant and two female participants. were recruited from
a university theater course in which the AAE dialect was discussed regularly throughout
the semester. The others were also members of the university community. Prior to
beginning the ratings. the investigator listened to the speech ofeach of these participants
and verified that each spoke AAE. Each participant signed a consent form (see Appendix
D) and passed a hearing screening with procedures identical to those described for the
listener participants. The naturalness raters were each compensated $10 for their time.

Naturalness rating task. Before making their observations. the naturalness raters
were informed that the purpose of their task was to select the most natural sounding
stimuli to be used in a study examining the perception of language use in African
American commtmities. Half of the naturalness raters were presented with one
randomization and the others received the remaining permutation. Distribution of the
randomization permutations was counterbalanced across gender.

The naturalness raters made their judgments in a university computer laboratory.
They viewed the PowerPoint presentations on computer screens and listened to the sets of
stimuli through earphones. No more than two naturalness raters participated at one
time. All naturalness ratings occurred over a three-hour period of time. When two raters

were participating simultaneously. they rated different randomization compilations.

68

The naturalness raters made a total of 36 ratings. Each rating was made after the
raters listened to all three phonologically and lexically consistent sentence recordings in
each group. They listened to each set and selected the one sentence that was the most
“natural sounding”. Each rater advanced the slides of the presentation and determined
when the recordings would play by clicking a mouse at a self-determined rate. The
investigator conducted a brief orientation session prior to listening to the stimuli to

familiarize the listeners with how to adjust the volume and advance the slides. At the

 

beginning of the presentation. they were presented with the following context scenario
and instructions:
Context Scenario

Imagine that the sentences you are about to hear were spoken by the
mother ofa teenager. The mother is talking on the phone about her upcoming
weekend vacation. She and her husband are leaving their teenage son at home.
and they suspect that he is planning to have a wild party. She is very concerned
that when they return on Monday. their house will be in shambles. She is
envisioning what their home will look like when they return.

Each screen will contain a sentence that is read three times. Click on each
of the three speaker icons until you have listened to all of the sentences on the
page. When you have heard all three sentences on the page. identify the one that
sounds the most natural (or similar to the way you might actually hear someone
say it in daily conversation). Write the letter of your choice on the form [see
Appendix E]. You may listen to the sentences as many times as you wish. The
whole process should take you 15-30 minutes to complete.

Since all of the sentences in the sets were identical with respect to content and planned
phonological alternatives. the naturalness raters were forced to base their choices on
suprasegrnental and digitization quality differences present among the three versions of
each sentence.

Naturalness rating results. A complete presentation of the naturalness rating

results is included in Appendix F. Every stimulus item was selected by at least half of the

69

participants. and no stimulus item was selected solely by raters from one stimulus
compilation. Twenty—eight percent (10/36) of the stimulus sentences was selected by
only halfof the naturalness raters. while the remaining 72% (27/36) was selected by over
half of the naturalness raters.

Analyzed according to speakers. speaker I received consensus of over halfof the
naturalness raters on 83% (15/18) ofthe stimulus sentences. and speaker 2 received
consensus ofover halfof the naturalness raters on 61% (7/18) of the stimulus sentences.
Two stimulus sentences. one for each speaker. received unanimous support from the
naturalness raters. Both of the sentences receiving unanimous support from the
naturalness raters involved the phonological pattern t/th. There was one tie on stimulus
sentence number 31 (speaker 2) In this case. three ofthe naturalness raters chose
recording A and three chose recording B. The investigator listened to the three
recordings from stimulus sentence number 31. and judged recording B to be the most
natural sounding.

Randomization and Cotmterlutlancing ofStimulus Sentences

The digital files for each recording selected during the naturalness rating process
were embedded into slides of PowerPoint presentations. F our different randomized
stimulus compilations were nrade. The sentences were randomized and counterbalanced

to control for order effects related to: (a) speakers and (b) tasks (see Table 5).

70

Table 6

Speaker Order and Task Order for Each Stimulus Compilation

 

 

 

 

 

 

 

 

Compilation Speaker
Number Order Percegtual Rating Order
1 1-2 Comprehensibility-Detectability
2 2-1 Comprehensibility-Detectabilit
3 2-1 Detectability-Comprehensibility
4 1-2 Detectability-Comprehensibility

 

Speaker order. For the dialect detectability ratings. recordings for the two
speakers were presented to the participants one at a time in blocks of 18 sentences (e.g.
18 sentences spoken by one speaker followed by 18 sentences spoken by another
speaker). For the comprehensibility ratings. the stimulus sentences were presented on
slides six at a time grouped according to salience and number of planned AAE features.
Within these groups of six sentences. three recordings from one speaker were placed on
the left side of the screen and three from the other speaker were placed on the right side
of the screen. The words “Speaker I” appeared over the left set of sentences. and the
words “Speaker 2“ appeared over the right set of sentences.

The speakers were segregated and clearly delineated in both tasks to control for
the processing costs that could be associated with randomly interrnixing the speakers.
Pisoni and Lively showed evidence of increased mental processing load expected to be

associated with rapidly switching attention to different voices (Pisoni & Lively. 1995).

71

 

The order ofthe speakers was varied across the compilationsz. Specifically. during the
dialect detectability ratings. two of the compilations had eighteen sentences read by
speaker I first. and two had eighteen sentences read by speaker 2 first. For the
comprehensibility ratings. two of the compilations had speaker I first on every slide. and
two had speaker 2 first on every slide. Within each compilation. the speaker order was
kept constant between the two rating tasks. That is. if speaker I was first in the
detectability presentation. then speaker I would also be presented first in the
comprehensibility presentation.

Task order. Two of the compilations had the participants rate comprehensibility
first. while the remaining two had the participants rate dialect detectability first. This
counterbalancing of task order is similar to what was done by Munro and Derwing
(1995b). They reported no order effects. In contrast. Gass and Varonis (1984) reported
that listeners found sentences to be more intelligible after they became familiar with the
speakers and context. This finding suggested that there could be a difference between the
groups of listeners who rate comprehensibility first and those who rate comprehensibility
last.

Sentence randomization. Niedzielski (1999‘) found that listener expectations can
affect the way that sentences are perceived. If listeners hear few dialect features in the
beginning. they may prej udge the speaker as a non-dialect user. Therefore. the sentences

that follow may be perceived differently from the scenario in which many features are

 

2 The actual speaker order was counterbalanced across the compilations. However. in the
comprehensibility rating task the words “Speaker 1" always appeared on the left side of the screen. and the
words “Speaker 2“ always appeared on the right side of the screen. regardless of which speaker was
actually being played. Likewise. in the dialect detectability rating task. the words “Speaker 1” always
preceded the first set of sentences and “Speaker 2” always preceded the second set of sentences.

heard in the beginning. To control for effects of presentation order. the sentences read by
each speaker were randomized (Dallal. 2003) for each presentation. Since each
participant heard the eighteen sentences read twice during the dialect detectability ratings
(once by each of the two speakers). there were a total of eight randomized permutations
for the dialect detectability ratings (two permutations for each of the four compilations).

For the comprehensibility ratings. the items were presented in six groups ofsix
sentences each (see Table 2). The six sentence groups contained three sentences read by
one speaker and the same three sentences read by the other speaker. The three sentences
were randomized within each sub-group. and the groups of six sentences were
randomized with separate permutations in the four different stimulus compilations.

Collection of Data

Presentation ofStimu/i

The experiment took place in a public school office space with individual
computers at different cubicles. The listener participants attended to a PowerPoint
presentation similar to the ones used by the naturalness raters. They viewed the
presentations individually on personal computers. No more than four listeners
participated at one time over a span of three weeks. Each listener was randomly assigned
to one of the four stimulus compilations. such that each compilation was rated by four
listeners. They each listened to the stimulus items through earphones. The investigator
instructed the participants on adjusting the volume and advancing the slides before they
began the presentation. They each indicated their perceptual judgments on two
computer-readable “bubble” forms (one for comprehensibility ratings and one for

detectability ratings) using a #2 pencil. After completing the tasks. they enclosed the

73

form in an envelope and gave it to the investigator. They did not provide any identifying
information on the form.
Rating Tasks

Two phases of the rating tasks will be described: 1) task orientation and
familiarity. and 2) stimulus presentation.

Task orientation amljanziliarization. Prior to each rating task. the participants
viewed a set of instructional slides that oriented them to the task and context of the
sentences (the same context provided to the naturalness raters. presented on page 68).
Each rating task was preceded by a familiarity slide.

The familiarity slide was designed to provide a consistent point of reference for
all of the participants. This was done by allowing them to experience a neutral set of
speech patterns (i.e.. no AAE features present) before listening to various pronunciation
patterns. For example. if some of the participants were presented with a stimulus
sentence containing three highly salient AAE features. then those listeners might perceive
the following sentences differently than those presented with fewer highly salient
features. Without hearing the sentences devoid of AAE features first. it would have been
unclear. in these instances. as to what in their individual repertoire of experiences they
were basing their judgments on. By presenting the sentences without any planned AAE
features prior to the rating task. it was assumed that when the first item was presented.
each listener made herjudgrnents based on the sentences without AAE phonological
features present.

The familiarization phase also oriented participants to each speaker’s voice to

decrease processing load. As discussed previously. if the speaker‘s voice changed

74

without orientation. then there could be increased processing load associated with
switching attention to another speaker (Pisoni & Lively. 1995). Thus. the familiarization
task also served to clearly delineate to listeners when they were about to hear a different
speaker and to familiarize them with the voices they were about to hear.

Speaker familiarization was slightly different for the two rating tasks. Prior to the
comprehensibility rating task. the participants clicked on a set of icons over which read
“Speaker 1.” They then listened (but did not rate) the six sentences read by the first
speaker on the compilation that contained no planned AAE phonological features. To the
right of the “Speaker 1” icons. they clicked on a set of icons over which read “Speaker
2.” They then listened to all of the sentences read by the other speaker that contained no
planned AAE phonological features. The sentences read by the different speakers were
clearly labeled as “Speaker 1" and "Speaker 2“ in the comprehensibility ratings that
followed. An orthographic transcription ofeach sentence was provided.

Prior to rating 18 sentences read by each speaker for the dialect detectability
rating task. the participants listened to the same six sentences used for “Speaker 1”
familiarization in the comprehensibility rating task. They then continued to rate the 18
sentences read by the first speaker. After rating the 18 sentences read by the first
speaker. they then heard the same “Speaker 2” familiarization recordings used in the
comprehensibility rating task. They then continued by rating the 18 sentences read by the
second. speaker.

Prior to the familiarization task. the following instructions were provided in the
PowerPoint presentations for the two tasks. Although the following descriptions depict

the scenario in which comprehensibility ratings occur first. the two task descriptions were

75

 

reversed when the detectability ratings occurred first.
Comprehensibility. The following instructions were provided to the participants
on the PowerPoint presentations prior to rating comprehensibility.

Task 1

1. You are about to listen to six groups of six sentences each. Three of the
sentences in each group will be read by one speaker. and three will be read by
another speaker.

2. After listening to both speakers in each group of sentences. you will rate
the speakers regarding how easily understandable you believe they would be to
people in the general population.

3. Anytime you see the ‘11 icon. click on it and listen to the audio clip.
4. Click on the arrow on the screen to advance to the next group of
sentences.

Making Your Ratings

- Imagine that you have these two speakers as clients. and you need to
determine (based on the overall sound of their speech) how understandable they
are likely to be to people in the general population.

0 After you hear all of the sentences spoken by the two speakers. fill in the
bubble on the response sheet that corresponds to a letter from A to E (A= Very
difficult to understand. E= Very easy to understand).

0 In this task you are rating all of the recordings from the two speakers as a
whole. You are not rating the speakers individually. If you feel that there is a
difference between how easily understandable the two speakers are. then think of
an average rating for the two.

0 Make sure that the number on the form matches the one on the
computer screen and make sure that you fill in the bubble completely.

- If you need to listen to the sentences again. you may do so as many times
as you wish.

0 If you make a mistake and need to go back. right-click somewhere on the
screen and select ‘previous.’ Then continue as before.

- After you finish rating the sentence groups. you will be asked to rate the

same sentences again in another way. But you’ll learn about that later.

Additional Information

° The whole experiment (including both ratings) should take about 25-35
minutes to complete.
0 You may stop this experiment at anytime for whatever reason and

discontinue your participation in the study without any further questions.”
Before the listeners began their ratings. the same scenario given to the

naturalness raters (see page 68) was presented to orient them to the lexical and odd

76

pragmatic characteristics of the sentences they were about to hear. The familiarization
slide was then presented. After completing the familiarization slide. the listeners
continued to the first comprehensibility rating.

On each screen. the item number was presented at the top of the page. Below
that. a table similar to Figure 4 was shown in which the listeners clicked on each speaker

icon to hear the speech recording.

Figure 4

Example Stimulus Table for Comprehensibility Ratings

 

 

 

 

Sentence Speaker 1 Speaker 2
A tooth may be with my list Monday. (i 5 (Q
A desk may be with my teeth Monday. (if; (E
A guest may be with my desk Monday. (E (E

 

 

 

 

 

A question was displayed beneath the table asking. “How understandable do you
think that these speakers would be to people in the general population?" The rating scale
ofA through E was presented under the question with “Very difficult to understand”
located under the “A” and “Very easy to understand” located under the E. The
participants rated the sentences by filling in the appropriate bubbles on a cornputer-
readable response sheet. The investigator supervised the participants to ensure that no

unexpected problems arose during the comprehensibility ratings.

77

The orthographic transcription of the sentences was included for several reasons.
First. the SLP listeners were judging whether the speaker was likely to be understood by
the general population. Such a judgment required the SLPs to make their decision based
upon what the person was trying to say. Second. the inclusion of the orthographic
transcription somewhat replicated relational phonological analysis often used in
diagnostic sessions. Third. providing a transcript of the sentences to be rated controlled
for the fact that intelligibility is likely to increase as the experience oflistening to a
speaker increases. Therefore. later sentences could have had an increase in
comprehensibility ratings that were more based on the experience listening to the
individual speakers and less based on the presence or absence of AAE phonological
patterns. Finally. providing a transcript ensured that the SLPs‘ ratings of projected
comprehensibility were not altered because of reduced intelligibility. Due to the fact that
the sentences were phonologically controlled. the stimulus sentences were semantically
odd. If no transcript was provided. it would be unclear whether the SLPs really
understood the intended sentence at all. The ratings obtained could have been for
sentences that the listeners thought they were hearing. rather than the actual sentences
intended. Thus. the inclusion of an orthographic transcript ensured that the SLPs rated
the intended sentence with the dialect features under investigation and that they didn’t
rate misinterpreted sentences resulting from unintelligibility.

After they listened to all of the sentences. they viewed a screen that read as

follows:

END OF SECTION ONE.

° Now take a 5-minute Break.
0 You may put your response sheet in the envelope.
0 After 5 minutes. click on the “next" arrow to continue.

78

Dialect Detectability. The following instructions were included for the rating of
dialect detectability:

Task 2

° The speakers of the sentences you have heard so far3 speak a dialect called
African American English. a dialect that is commonly spoken by many African
Americans.

0 You are about to hear the same sentences again.

0 This time you will rate them regarding how noticeable the use of African
American English is.

0 You will rate each sentence individually. rather than in groups this time.

’ You will make a total of 36 ratings for this task.

Making Your Ratings

1) After listening to each sentence. consider how noticeable the use ofAfrican
American English (AAE) is in each sentence.

2) Base your judgment not on the overall message of the sentence. but rather. on
how the whole sentence sounds to you.

3) Indicate your responses by filling in a bubble next to each item number that
corresponds to a scale from A to E: A: Not noticeable at all. E= Extremely
noticeable.

Additional Information

1) After you finish making these ratings. you are done with the experiment.

2) However. as previously mentioned. you may stop this experiment at any point.

for any reason. without further explanation. and without penalty.”

The listeners were then presented with another familiarization slide that oriented
them to the sentences that they heard during the detectability ratings and the sound of the
speaker's voice that followed. The slides were formatted such that the item number was
located at the top of each slide. The orthographic transcription of the sentence was

centered on the screen with a speaker icon located below. The rating scale was presented

on each slide directly under the question “How noticeable is the dialect of African

 

3 The words “you have heard so far” were replaced with the words “you are about to hear" when the dialect
detectability task was first. Similar modifications were made that between the instructions for the two tasks
with respect to their ordering on different compilations.

79

American English in this recording?” The listeners indicated their responses on a
different computer-readable sheet. Upon completing the ratings. the listeners were
presented with the following slide: “END OF EXPERIMENT. Thank you for
participating in this study! Please place your response sheet in the provided envelope and
hand it to Greg”
Intra-Rater Reliability

Four participants (one from each compilation. 25% of the participants) were
randomly asked to rate the same stimulus compilation that they had rated previously.
The second rating occurred two weeks after the first presentation to determine intra-rater
reliability for both tasks. During the second rating. the reliability raters participated in
the same environment and on the same computer that they used in the first rating.

Data Processing

The computerized response sheets were given to University Scoring Office at
Michigan State University for processing. The scoring office sent the investigator a data
file over electronic mail. The data were imported into Excel 2002 for Windows and
SPSS. version 12.0. for analysis.

Data Analysis

The analysis of the data occurred in two phases: (a) compilation comparisons. (b)
task-specific comparisons.
Compilation Comparisons

The compilations were compared using a one-way Analysis of Variance
(ANOVA). This was followed by exhaustive multiple comparisons using paired t-tests to

determine the source of the differences among the four different compilations. Type I

80

error rates were controlled using the Bonferroni method. Ratings from identical stimuli
among the compilations were matched during the paired t-tests. The overall comparison
among the compilations were followed by: (a) a compilation comparison specific to the
dialect detectability task and (b) a compilation comparison specific to the
comprehensibility task. Similar to the overall compilation comparisons. one-way
ANOVAs were used for these analyses. and exhaustive multiple comparisons using
Bonferroni paired t-tests followed when differences were detected in the omnibus
ANOVA tests.
T ask-Specific Comparison of Test Variables

Three within-subjects factors were included in the omnibus analyses of the dialect
detectability data: (a) speaker (2 levels). (b) frequency ('3 levels). and (c) salience (2
levels). Due to differences in the methods specific to the comprehensibility ratings. the
speaker factor was not included. Therefore. the dialect detectability data were analyzed
using a parametric 2 x3 x 2 ANOVA. and the comprehensibility data were analyzed
using a parametric 3 x 2 ANOVA. Both ANOVAs also included the between-subjects
factor compilation. Such analyses were followed by paired t-tests to detect the origin of
differences revealed by the ANOVAs. When t-tests were used to compare more than two
levels of a variable. the Type I error rates were controlled using the Bonferroni method.

Some statisticians have suggested that nonparametric statistics should be used
when analyzing ordinal. Likert scale data in lieu of parametric statistics (Schiavetti &
Metz. 1997); however. Thomas (1998) demonstrated that parametric statistical analyses

could be used reliably with Likert scale data. as was generated by this study.

81

Repeated-measures statistics were used because the comparisons made in this
study were most often between specific stimuli. not between distinct groups of
participants. Therefore. the comparisons between specific stimuli within and across the
compilations were analyzed as within-subjects factors. When differences among the
compilations were analyzed. the compilation variable was maintained as a between-
subjects factor.

This study‘s results were informed by the use oftwo different effect size
measures. When the results of a repeated measures ANOVA were reported. the effect
size measurement partial eta square (ifp) was used. This measurement is the proportion
of total variation in the data attributable to the effect of the variable itself. excluding other
factor effects from the total nonerror variation. The partial eta square measure ranges
from 0-1 (Pierce. Block. & Aguinis. 2004). When the results of paired t-tests were
reported. the effect size measure was C ohen‘s d (Cohen. 1988). This measure is
calculated by dividing the mean difference by the standard deviation. C ohen‘s standard
was used for interpretation. as follows: .20< d< .50 = “small”. .50<d<.80 = “medium”.

and d>.80 = “large”.

CHAPTER III
RESULTS

This dissertation sought to determine ifAnglo-European American SLPs‘
perceptions of dialect detectability and comprehensibility were influenced by the number
(frequency) and type (salience) of AAE phonological features present within the
sentence. This overall objective was divided into two measurements: (a) dialect
detectability. and (b) comprehensibility. The research questions for these two
components are:

a) Dialect Detectability: Are Anglo-European American SLPs’ perceptions of
dialect detectability influenced by the number and type of AAE phonological
features included in the sentence?

b) Comprehensibility: Are Anglo European American SLPs’ perceptions of
comprehensibility influenced by the number and type ofAAE phonological
features included in the sentence?

This section of the dissertation begins by reporting intra-rater reliability for
randomly selected participants. Next. a comparison follows to report differences among
the four different stimulus compilations. Detection ofdifferences among the
compilations was required to determine how the compilations may be pooled for the
subsequent analyses of the experimental independent variables. Effects specific to the
dependent variables. dialect detectability and conzprehensibility. are reported as the the

principal findings.

83

Intra-rater Reliability

One participant from each stimulus compilation was randomly selected to
complete her ratings again. Each reliability participant rated all items a second time two
weeks after the first time on the same computer and in the same enviromnent. The
ratings associated with the 42 individual stimulus items for each of four reliability
participants were matched for time 1 and time 2. The ratings were analyzed for
association between time I and time 2 using a paired samples correlation test. Hayes and
Hatch (1999) argued that using correlation measures for reliability testing may be
superior to using percentage of agreement because: (a) percentage of agreement does not
automatically consider effects due to chance. and (b) chance agreements are strongly
influenced by data variability. data plateaus. and by scoring of near agreement as absolute
agreement. The item-matched correlation comparison resulted in an N equal to 168 for
the comparison over both tasks (42 stimulus sentences times 4 participants).

When the ratings specific to dialect detectability and comprehensibility were
analyzed together. the test-retest reliability ratings were highly correlated. r(168)= .90.
p<.001. The analysis revealed that 8 % of the variance was accounted for by the
association between time 1 and time 2 ratings. r3: .81.

In a follow-up reliability analysis. separate item-matched correlation comparisons
were preformed specific to dialect detectability ratings and comprehensibility ratings. In
the dialect detectability reliability analysis. only the 36 dialect detectability ratings
generated by each of the 4 reliability participants were included. reducing the N to from

168 to 144. Time 1 and time 2 measures specific to the dialect detectability ratings were

84

also highly correlated. r( 144) = .92. p<.001. This analysis revealed that 85% of the
variance was accounted for by the association between time 1 and time 2 ratings. r3: .85.

In the reliability analysis of comprehensibility. only the 6 comprehensibility
ratings for each of the 4 participants were included. resulting in an N: 24. Time 1 and
time 2 comprehensibility ratings were significantly. but not as highly correlated as the
detectability ratings. r(24.)= .740. p<.001. This analysis revealed that 5 % of the
variance was accounted for by the association between time 1 and time 2 ratings. r3: .55.
Due to the reduced reliability revealed in the comprehensibility data. the ratings were
analyzed for difference using a paired t-test. This analysis showed that the ratings were
not significantly different. t(23)=-.419. p=.679.

Compilation Comparisons

Compilation was the only between-subjects factor in this study. Therefore. each
of the four compilations included four of the sixteen total participants. Before analysis of
the independent variables could be done. it was necessary to determine if there were
substantive differences among the ratings elicited by the four compilations. If the ratings
were similar among the compilations. then the data associated with the different
compilations could be pooled in the subsequent analyses. If any of the compilations
elicited statistically different ratings for either of the two tasks. then only those
statistically similar could be pooled in the subsequent analyses. Compilation differences
were analyzed: (a) over both tasks. (b) specific to comprehensibility ratings. and (c)

specific to dialect detectability.

85

Compilation Comparisons Over Both Tasks
A one-way ANOVA was used to compare the data elicited by the four different
compilations. The ratings for the 42 individual stimulus items in each of the different
compilations were compared for the 16 subjects (N = 672). This omnibus analysis
revealed significant differences among the four stimulus compilations. F (3. 668') = 4.272. .4.
p = .005. Due to the significance of this test. a multiple comparison analysis was used to
detect the origin of the differences. In this analysis. the 42 ratings from the four
participants in each compilation (N—=l68) were matched according to item similarity. The
multiple comparisons analyses revealed that the ratings for three of six comparisons were
significantly different. Table 7 shows the mean difference between the ratings for each
paired compilation analysis with the standard deviation of the differences. The t-score is
displayed with its probability value and Cohen's d. The Bonferroni technique was used
to reduce the alpha level from .05 to .008 to control for Type I error.
The differences revealed by these analyses suggested that the characteristics of
different compilations (i.e.. task ordering and/or speaker ordering) may have influenced
the ratings of comprehensibility and/or detectability. The ratings for comprehensibility

elicited by each of the compilations were then analyzed for differences separately from

the ratings for dialect detectability.

86

Table 7

Differences Among the Four Compilations Over Both Perceptual Rating Tasks

(Four different listener ratings for 42 stimulus sentences in each compilation. N=l68)

 

 

 

t(l67)= 2988*.
p = .003. d= .23.
small effect size

Stimulus Compilation 2 Compilation 3 Compilation 4
Compilation 2-1. C-D 2-1. D-C 1-2. D-C
Compilation 1 Md=.530. SDd= 1.07 Md: .208. SDd=1.52 Md: .393. SDd=1.30
1-2. C-D t( 167) = -6.406*. t(l67)= -1 .781. t(l67)= -3.918*.
p<.001. d= .49 small p = .077 (ns. p<.001. d= .30.
effect size p>.008). small effect size
cl: .14
Compilation 2 Md= .321. SDd= Md: .137. SDd=l .12
2-1. C-D 1.40 t(l67)= 1.583 (ns).

p = .115 (ns. p>.008_).
d= .12

 

Compilation 3
2-1. D-C

 

 

 

 

Aid: .185. SDtl=1.77
t(l67)= -l .354 (ns).

p = .178 (ns. p>.008).
d= .10

 

Abbreviations: Md= Mean Difference. SDd= Standard Deviation of the Difference. C =

Comprehensibility Task. D= Dialect Detectability Task. 1: Speaker 1. 2= Speaker 2.

Compilation labels denote task order followed by speaker order (e.g. 1-2. C-D = Speaker

1 followed by Speaker 2. C omprehensibility followed by Dialect Detectability)

* Differences are significant at alpha of .05 (p<.008 Bonferroni adjustment).

Compilation Comparisons Specific to Comprehensibility

For the compilation comparison specific to comprehensibility. a one-way

ANOVA was performed with the four-level. between-subjects factor ofcompilation. In

this case. only the six ratings generated during the comprehensibility task were included

from each of the 16 participants (N=96). The four compilations did not elicit statistically

87

 

different ratings for comprehensibility. F ( 3. 92): 2.462. [F .068. This indicated that the
differences revealed in the overall comparison of the compilations originated primarily in
the dialect detectability data.

Compilation Comparisons Specific to Dialect Detectability

For the analysis of the compilation differences associated with the dialect
detectability data. a one-way ANOVA with the four-level. between-subj ects factor
compilation was used. The 36 ratings from each of the 16 participants were analyzed
across the four compilations (N= 576). The dialect detectability ratings among the four
compilations were statistically different. F (3. 572) = 5.246. p = .001. To determine the
origin of the difference. an exhaustive multiple comparisons analysis was done using
paired t-tests. matched for item similarity across compilations. Table 8 shows the mean
differences ofthe dialect detectability ratings between the various compilations with the
corresponding standard deviations of the differences. The t-scores are reported with the
probability values and C ohen‘s d scores. Although some of the compilation differences
had p-values less than .05. the Bonferroni-adj usted alpha is .008 to control for Type I
error. Therefore. only those t-scores with corresponding probabilities less than .008 are
considered significant.

The summarized data in Table 8 shows that compilation l was statistically
different from both 2 and 4. but not statistically different from compilation 3.
Interestingly. compilations 2 and 4 were very similar. both eliciting the same mean
dialect detectability score. While the mean scores in compilations 1 and 3 were slightly

(not significantly) different. the standard deviations for the two were the same.

88

 

Table 8

Differences Among the Four Compilations Specific to the Dialect Detectability Task

(Four different listener ratings for 36 stimulus sentences in each compilation. N=144)

 

 

t(l43)= -6.515*.

p<.001. d= .54
“medium” effect
size

t(143)= -2.301.
p=.023 (ns. p>.008)
d= .19

Stimulus Compilation 2 Compilation 3 Compilation 4
Compilation 2-1. C-D 2-1. D-C 1-2. D-C
Compilation 1 Md; .576. SDd= Mcfi .285. SDd= Md: .576. SD: 1.24
1-2. C-D 1.06 1.49 t(143)= -5.562*.

p<.001. d= .46
"small” effect size

 

Compilation 2
2-1. C -D

Md= .292. SD: 1.44
((143): 2.434 (ns).

p = .016 (ns.
p>.008).
d= .20

Md: .000. SD: 1.10
t (143): .000.

p = 1.00 (ns. p>.008).
(1:.00

 

Compilation 3
2-1. D-C

 

 

 

 

Md: .292. 1.79

t(143)= -1.961 (ns).
p = .052 (ns. p>.008).
d: .16

 

Abbreviations: Md: Mean Difference. SDd= Standard Deviation ofthe Difference. C =

C omprehensibility Task. D= Dialect Detectability Task. l= Speaker 1. 2= Speaker 2.

Compilation labels denote task order followed by speaker order (e.g. 1-2. C -D = Speaker

1 followed by Speaker 2. C omprehensibility followed by Dialect Detectability)

* Differences are significant at alpha of .05 (p<.008 Bonferroni adjustment).

Since the compilations were counterbalanced according to speaker order and task

order. it was possible that one or both of these factors caused different dialect

detectability ratings. The compilations were ranked according to their mean detectability

ratings. and the characteristics of each compilation were presented alongside the ranking

89

 

(see Table 9.). The ranking revealed that neither speaker order nor task order had a
consistent effect. yet an interaction could be inferred between these two variables. Task
order was significant when speaker I was presented first (detectability-comprehensibility
> comprehensibility-detectability). Speaker order was significant when
comprehensibility ratings were first (speaker order 2-1 > speaker order 1-2). No

. . . . . . . . . 4
theoretical justification for such an interaction could be interred .

Table 9
Compilation Characteristics and Rank Ordering According to Average Dialect

Detectability Ratings

 

 

 

 

 

Rank Compilation Speaker Mean N
Number Order Percefltual Rating Order Detectability SD

1 2 2-1 Comprehensibility-Detectability 3.22 1.47 144

2 4 1-2 Detectability Comprehensibility 3.22 1.57 144

3 3 2-1 Detectability-ComprehensibilitL 2.93 1.37 144

4 1 1-2 Comprehensibility-Deteetability 2.65 1.37 144

 

 

 

 

 

 

 

 

(.‘ompilt‘ttion Groupings Resulting Front (.‘ompilc‘ttion Differences
Although the reason for the differences across compilations was not determined.

the data associated with the four compilations were grouped according to statistical

 

4 It is possible that despite the use ofcontrolled exposure to phonologically consistent recordings across the
compilations and randomized orderings. stimulus ordering effects occurred nonetheless. To test the
possibility that early exposure to minimally or maximally detectable features influenced the ratings for the
rest of the sample. the first five stimuli presented in the detectability section of each compilation were
analyzed for differences. The detectability ratings were not statistically different within the first five
stimulus items across groups. F (3. 76): .353. p= .787. The stimuli were further analyzed qualitatively.

The first five stimulus items occurring on each compilation were coded regarding the frequency of features
contained in each item and the relative salience of the features. There were no consistent qualitative
differences in the groupings of stimuli pertaining to frequency or salience.

90

 

similarity. To determine statistical similarity. a hierarchical cluster analysis was used.
The squared Euclidian distance between the average dialect detectability ratings created
two groups. Compilations 2 and 4 received the highest ratings and comprised one group;
compilations 1 and 3 received the lowest ratings and comprised a second group.
Compilations 2 and 4 were designated as the high detectability group: compilations l and
3 were designated as the low detectability group. These two groups of 288 ratings (36
ratings from eight participants) were analyzed separately during the analysis of dialect
detectability. In the separate analyses for these two data groupings. the between-subjects
factor of stimulus compilation had two levels rather than four (N=144 for each level).

As stated previously. no significant differences were detected among the
compilations in the comprehensibility data. Therefore. the six comprehensibility ratings
generated from all 16 participants were analyzed together (96 total ratings). Thus. the
between-subject factor of compilation had four levels in the comprehensibility analysis
(N=24 for each level).

Principal Findings: Dialect Detectability Ratings
Description of F actors

Does the number ofAAEt/‘eatru'es and/or the perceptual salience of the AAE
features in a stin-zulus sentence affect the degree to which the listeners detect an AAE
dialect? The analysis used to answer this research question involved two methodological
variables (compilation and speaker) and two experimental variables (frequency and
salience). Of the two methodological variables. compilation was a between-subjects
factor and speaker was a within-subjects factor. Both of the experimental variables were

within—subj ects factors.

91

The methodological. within-subj ects factor speaker (2 levels) referred to the two
different speakers that were included in the study. These two speakers have been labeled
speaker I and speaker 2. The experimental variables. frequency (3 levels) and salience
(2 levels). were both within-subjects factors. Frequency referred to the number of dialect
featm‘es that were included in each of the stimulus sentences (0. 1. or 3.). Salience
referred to the relative perceptual salience of the features included in the stimulus
sentence (low or high). As described in Chapter II. phonological features were grouped
into categories of low salience and high salience according to the results of the
preliminary experiment examining the perceptual salience of the phonological patterns
presented in nonsymbolic syllables (see Appendix A).

The three within-subj ects factors described above yielded a 2 x 3 x 2 within-
subjects ANOVA. with a two-level between-subjects factor. compilation. ANOVAs of
this type were used as omnibus tests to detect differences and interactions among these
four factors in both the high detectability group analysis and the low detectability group
analyses. Due to the fact that each participant received the same stimulus items. the
within-subjects comparisons in the analyses were between stimulus items. not between
participants. When differences were revealed by the omnibus test. a paired t-test was
done that matched similar items between comparison stimulus groups. In which case. if
more than two groups were compared. the alpha was adjusted using the Bonferroni
method.

The following dialect detectability findings are organized as such: (a) high
detectability group results. (b) low detectability group results. (c) post hoc comparison of

features. and (d) summary of dialect detectability results.

High Detectability Group Results

Frequency effects. As hypothesized there was a significant main effect for
frequency in the high detectability group. F (2. 22)= 155.057. p<.001. indicating that as
the number of AAE features increased. so did the dialect detectability ratings. This effect
is displayed graphically in Figure 5. This effect was large. ifp= .876. indicating that
almost 90% of the total effect was accounted for by the variable itself. Table 10 shows
the differences between the average dialect detectability ratings analyzed for the three
frequency levels. As hypothesized. the stimuli containing three AAE features were rated
as more detectable than stimuli containing just one feature. t(95) = -9.3 56. p<.001.
Stimuli containing one feature were significantly more AAE-detectable than were those
containing no features. t(95)= -11.675. p<.001. While both comparisons had large effect
sizes. the effect of the 0-1 frequency difference (d= 1.19) was larger than the effect of the
1-3 frequency difference (d= .96).

There was not a significant frequency-speaker interaction. F (2. 44) = 1.254. p =
.294. indicating that the frequency effect was consistent across the speakers. Likewise.
there was not a significant frequency-compilation interaction. F (2. 44) = .177. p: .839.
indicating that the frequency effect did not differ significantly across the two

compilations in the high detectability group.

93

Figure 5

Average Dialect Detectability Scores in the High Detectability Group Obtained in

Relation to the Frequency and Salience of AAE Features (N=l6)

 

01
I

.p
1

Average Detectability Ratings
'1’ ‘t’

T

 

 

 

 

l l
O 1

l
3

Number of AAE Features

Salience
— Low
- - - High

Dialect Detectability Rating Scale: I: Not noticeable at all. 5: Extremely noticeable

94

Table 10

Sentences in the High Detectability Group With 0. 1. and 3 Features Analyzed for Dialect

Detectability (N= 96)

 

 

 

 

 

 

Frequency = 0 Frequency = 1 Frequency = 3
M: 1.71. M: 3.45. M: 4.51.
SD= 1.035 SD= 1.204 SD= .649
Difference 0-1 Difference 1-3
t(95) = -9.356 t(95)= -11.675
p<.001. d= 1.19 p<.001. d= .96

 

 

 

 

Salience effl’cts. Although there was a significant main effect for salience. F (1.
22) = 8.008. 1): .01 . in the high detectability group (see Figure 5). the effect is qualified
by a significant salience-speaker interaction. In other words. the salience effect varied
for the two different speakers. F (1. 22)= 20.129. p<.001. Specifically. speaker 2 elicited
a significant salience effect. t(_7l) = -4.512. p<.001. with a medium effect size. d= .53.
while speaker I did not elicit a significant salience effect. t(71) = .462. p = .645. There
was not a significant salience-compilation interaction. F (1. 22)= .249. p=.622.

F requency-sal ience interaction effects. There was no interaction between the
frequency and salience variables in the high detectability group. F (2. 44)= .740. p=.483.
Low Detectability Group Results

Frequency effects. There was a significant main effect for frequency in the low
detectability group. F (2. 44)= 159.135. p<.001. This effect was qualified by a significant
frequency-speaker interaction. F (2. 44)= 6.306. p= .004. and a significant frequency-

cornpilation interaction. F (I. 22)= 6.709. p=.003. These results indicated that the

95

frequency effects were different between the two speakers and the two compilations in
the low detectability group. Figure 6 illustrates that even though there were
methodological interactions between frequency and the two methodological variables.
they did not affect the overall direction of the effects. In all cases. increases in frequency
of AAE features resulted in increases in dialect detectability scores.

To determine the origin ofthe frequency—speaker and fi'equency-compilation
interactions. subsequent analyses were done. F irst. the analyses related to the frequency—
speaker interaction are reported. Then the analyses related to the frequency-compilation
interaction are reported.

To analyze how the frequency effects by the two different speakers differed. the
12 ratings from the 8 participants elicited by the 2 different speakers in the low
detectability group were analyzed separately (N= 96). These two speaker-specific
analyses were done using two separate. 2 x 3 ANOVAs which included the within-
subjects factors of salience and frequency and excluded the between-subjects factor
compilation. Both speaker I. F (2. 94)= 75.583. p<.001 . and speaker 2. F (2. 94)=
150.849. p<.001 elicited frequency effects. While both speakers elicited large frequency
effect sizes for the low detectability group. speaker 2 had a larger effect size. if}: .852.

than did speaker I. (72,): .617.

96

Figure 6
Average Dialect Detectability Ratings for the Two Speakers and the Two Compilations in

the Low Detectability Group Organized by Frequency and Salience

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

Speaker 1 Speaker 2
35— Salience 35‘ Salience
g to“; .g Low
624— ' '9 §4-— .. - - High
Q .é‘
E '5
£3 __ S ._l
.9.» 3 a 3
3 8
o __ t” _.
32 32
0 0
> >
< <
1-- 1—
l l I l l l
O 1 3 0 1 3
Number of Features Number of Features
Compilation 1 Compilation 3
U) 5— Salience 35— Salience
5’ .5 Low
.5 LOW '05 H' 11
iii - a: - ' lg
n. 4__ - - - Huh 4— r
= n
9 m
.33 3 g
3 o
a: 2- 8,2—
? e
‘1’ 2
2 1__ < 1....
l l l l 1 I
O 1 3 1 2 3
Number of Features Number of Features

Dialect Detectability Rating Scale: l= Not noticeable at all. 5= Extremely noticeable

97

The ratings elicited by the two speakers were examined separately for the
different frequency levels using paired t-tests with a Bonferroni-adjusted alpha of .025.
Table 1 1 is organized by speaker and shows the mean dialect detectability ratings for the
different frequency levels and the differences between the ratings associated with each
adjacent frequency level. Both speakers elicited a greater effect when the frequency of
dialectal features changed from zero to one than from one to three. However. speaker 2
elicited a greater range of detectability changes across all frequency levels than did

speaker I.

Table 11
Speaker C ornparison for the Mean Change in Dialect Detectability Scores Across the

Different Frequency Levels in the Low Detectability Group (N= 48)

 

 

 

Speaker 1 Speaker 2
Frequency Frequency Frequency Frequency Frequency Frequency
0 1 3 0 1 3
M: 1.67. M: 2.79. M: 3.54. M: 1.54. M= 3.15. M: 4.04.
SD= .975 SD= 1.166 SD= .944 SD=1.031 SD= 1.185 SD= .798

 

 

 

 

 

Difference 0-1 Difference 1-3 Difference 0-1 Difference 1-3

 

[(47): -7.323.
p<.001. d= 1.06

t(47)= 4803.
p<.001. d= .69

t(47)= -10.419.
p<.001. d= 1.50

t(47)= —6.859.
p<.001. d: .99

 

 

 

 

 

 

A multiple comparisons analysis using paired t-tests was done to compare the
differences between the two different speakers at the three different frequency levels. In
this analysis. six ratings were generated by the eight participants for each of the three

different frequency levels (N=48). These ratings were matched according to stimulus

98

similarity and were compared between speakers. Specifically. the 48 ratings elicited by
speaker I at the three-feature level were compared to the 48 elicited by speaker 2 at the
three-feature level. The same comparison between speakers was done for the one and the
zero feature levels.

Speaker 2 elicited higher detectability ratings when three AAE features were used
than did speaker 1. t(47)= -3.958. p<.001. The effect size of this difference was medium.
d= .57. In fact. the occurrence of three features was the only frequency level that differed
significantly between the speakers. They were not statistically different for stimuli
containing zero features. t(47)= 1.137. [F .261. or one AAE feature. t(47)= -2.1-4.
p=.036 (not significant at Bonferroni-adjusted alpha of .025).

Analyses were also done to determine the source of the frequency-compilation
interaction, F (1. 22)= 6.709. p=.003. When the ratings elicited by the two compilations
were analyzed separately for the three levels of frequency using 2 x 3 ANOVAs (only
including the within-subj ects factors of salience and frequency). both compilation 1, F ( 2.
46)= 149.352. p<.001. and compilation 3. F(2. 46)= 81.414.p<.001. elicited frequency
effects. However. compilation l elicited a greater effect size. ([21,: .869. than did
compilation 3. (ff .634.

A multiple comparisons analysis similar to the one used to examine speaker
differences across the frequency levels was used to examine compilation differences
across the frequency levels. The data presented in Table 12 shows that both compilations
elicited a greater change in detectability scores when the stimuli increased from zero
features to one feature than they did when the frequency increased from one feature to

three features. These similarities indicate that. although the frequency effects were

99

 

quantitatively different between the two low detectability group compilations. they were

not qualitatively different. Both compilations elicited similar types of frequency effects.

 

 

 

 

 

 

 

 

 

Table 12
Compilation Comparison for the Mean Change in Dialect Detectability Scores Across the A
,4
Different Frequency Levels in the Low Detectability Group (N= 48)
Compilation 1 Compilation 3 I
F req= O F req= 1 F req= 3 Freq: 0 F req= 1 F req= 3 ‘
M: 1.21. M: 2.90. M: 3.83. M: 2.00. M: 3.04. M= 3.75.
SD= .504 SD= 1.077 SD= .808 SD=1.203 SD= 1.288 SD= 1.000
Difference 0-1 Difference 1-3 Difference 0-1 Difference 1-3
t(47)= -10.876. t(47)= ~6.015. t(47)= -7.148. t(47)= -5.464.
p<.001. d= 1.57 p<.001. d= .89 p<.001. d: 1.03 p<.001. cf: .79

 

 

 

 

 

 

Salience efflcts. The low detectability group had a significant main effect for
salience. F (l. 22) = 14.749. p= .001. The effect size for the low detectability group was
medium. (72,): .401 Approximately 4 ' % ofthe salience effect elicited by the low
detectability group could be accounted for by the variable itself.

There was not a salience-compilation interaction. F(l . 22)= 2.880.p=.104. There
was also no salience-speaker interaction. F (1, 22)=.757. p=.394. as there was in the high
detectability group. F(l. 22)= 20.129.p<.001.

F requemjv by salience interaclicm. Similar to the high detectability group. the
low detectability group did not yield a frequency-salience interaction. F ('2. 44)= .084.

p=.919.

100

Post Hoc Comparisons ofFeaturesfbr Detectability Ratings

A post-hoc analysis was done comparing the dialect detectability scores
associated with the six individual AAE dialect features used in the detectability stimuli:
(a) final consonant deletion of/k/ (F Dk). (b) final consonant deletion of /t/ (F Dt). (c) final
cluster reduction of /k/ ( F Rk). (d) final cluster reduction of /t/ (F Rt). (e) final t/0
substitution (t/th). and (1) final f/0 substitution (f/th). Due to the fact that there were six
total features analyzed. the results would have been too complex and beyond the scope of
this dissertation to explore differences between rankings according to the methodological
variables compilation and speaker. Therefore. to simplify these post hoc results. all
methodological factors were pooled in the analyses.

These post hoc analyses aimed to answer the following questions: 1) Which
dialect features elicited the highest dialect detectability ratings and which elicited the
lowest? In other words. what is the ranking of the dialect features with regard to each
one’s influence on dialect detectability ratings? 2) Was the perceptual salience
classification accurate as measured by the actual detectability of features observed in this
study? In other words. was each individual feature included in the high salience group
more detectable than each of the features included in the low salience group?

Ranking (go/:feamres. To determine each feature’s individual detectability value. a
calculation was done. The detectability ratings elicited by sentences containing just one
feature were compared to sentences that had an unfulfilled opportunity for that same
feature to occur (i.e.. the control stimulus). This new calculation was called the mean D
score. For example. the mean D score for F Dk was obtained by subtracting the perceived

dialect detectability ratings of sentences that had an unfulfilled opportunity for F Dk (and

101

 

no other planned features) from the ratings of sentences that had one instance of F Dk
(and no other planned AAE features). The mean D score associated with each
phonological feature was ranked. Paired t-tests matched for stimulus similarity were
used to determine if the differences between the zero-frequency levels were significantly
different from the one-frequency levels for each dialect feature. In addition to these
values. the effect sizes for the differences between the two compared frequency levels for
each dialect feature were reported in Table 13. The Bonferroni-adj usted alpha was .008.

Table 13

Ranking of Features by Mean D Scores (N=32 for each feature)

 

 

 

 

 

 

 

 

Ranking Feature Mean D- SD 1 (31) p Cohen’s d value
score (M/SD)

1 /k/-deletion 2.19 1.091 -11.346 <.001 2.01 (large)

2 f/th 1.94 1.014 -10.809 <.001 1.91 (large)
substitution

3 /sk/-cluster 1.66 .902 -10.388 <.001 1.84 (large)
reduction

4 /st/ cluster 1.06 .948 -6.338 <.001 1.12 (large)
reduction

5 t/th 1.56 1.480 -5.973 <.001 1.06 (large)
substitution

6 t-dclction .91 1.729 -2.964 .003 .52 (medium)

 

 

 

 

 

 

 

Note: Mean D scores were derived by subtracting the detectability ratings for the zero

frequency level from the detectability ratings for the one frequency level.

In this ranking ofphonological features. it is particularly interesting to examine
the top three and bottom three ranked features (see Table 13). The bottom three features
in the ranking. all involved a /t/ in the final position (F Rt. t/th. and F Dt). The top three

features did not involve a /t/ (F Dk. f/th. and F Rk). The phoneme /t/ has been cited as

 

especially prone to deletion in many dialects. including those considered “standard”
(Guy. 1980; Stockman. 2006). It is possible that final /t/ is so prone to deletion among
many American speakers that the listeners didn’t perceive its presence or absence as
much as the other features that were devoid of a final /t/ alternation. F urthennore, the
environment for final /t/ was preconsonantal. Such an environment may be especially
prone to deletion of final /t/ by a wide array of American English speakers (Guy. 1980).

Perceptual salience classification and detectability. The next analysis aimed to
determine ifthe salience classification was accurate for each feature. The mean D scores
for each high salience feature were matched to the mean D scores for a phonetically
comparable low salience feature. The comparable features matched in this analysis were:
1) FDk (High)--FRk (Low); 2) F Dt (High)--F Rt (Low). and 3) t/th (High)--f/th (Low).
Paired t-tests were used to determine if there were significant detectability differences
between the components of each pair (see Table 14). The Bonferroni-adjusted alpha was
.02.

Only the F Dk and FRk comparisons reached significance. as shown in Table 14.
The other two comparisons. although not significant. tended in the opposite direction than
was expected. In both cases. the features classified as low salience had higher mean D
scores than did the phonetically comparable high salience features (cf. 1.94 and 1.56 for

f/th and t/th. respectively).

Table 14
Comparison of Mean D Scores for Comparable Features in the High and Low Salience

Groups (N=32)

 

 

 

 

 

 

 

Salience Feature Mean D SD Difference SD t( 31) p
.531 1.107 2.715 .010*
Low FRk 1.66 .902
High FDt .91 1.721
-.156 1.936 -.456 .651
Low F Rt 1.06 .948 (ns)
High t/th 1.56 1.480
-.375 1.385 -1.531 .136
Low f/th 1 .94 1 .014 (ns)

 

 

 

 

 

 

 

 

 

 

Note: Mean D scores were derived by subtracting the detectability ratings for the zero
frequency level from the detectability ratings for the one frequency level.

* Significant. p<.02 (.05 alpha adjusted per the Bonferroni method)

Summary of Dialect Detectability Results

The data supported the hypothesis that the number of dialect features included in
the sentence would increase the perception of dialect detectability. The issue of
perceptual salience was not as clear. In some instances. the perceptual salience of the
phonological patterns had a moderate effect on the ratings of dialect detectability.
However. for speaker 1 in the low detectability group. there was no salience effect.
These inconsistent findings may be partially explained by the post hoc analysis. The post

hoc analysis revealed that the perceptual salience of some phonological patterns did not

104

explain the dialect detectability ratings elicited in the study. These findings will be
examined further in the upcoming discussion.
Principal Findings: Comprehensil)ility Ratings

Does the number of AAE features and/or the perceptual salience of the AAE
features in a stimulus sentence affect the degree to which the listeners deem a speaker as
comprehensible to the general population? In answering this question. it is important to
consider that comprehensibility was measured in a way that was different from dialect
detectability. During the rating of comprehensibility. the listeners were asked to assign
one rating to both speakers. so the methodological. within-subjects factor speaker does
not exist in the comprehensibility analysis. All other factors (salience. frequency. and
compilation) remained. As discussed previously. the different stimulus compilations did
not elicit significantly different comprehensibility ratings. F (3. 92): 2.46. p= .068.
However. compilation was retained as a between-subjects factor in the analysis to
determine if either of the within-subjects factors interacted with compilation. Therefore.
a 2 x 3 repeated measures ANOVA with a four-level between-subjects factor.
compilation. was used to analyze the comprehensibility ratings. As occurred in the
dialect detectability analyses. paired t-tests adjusted using the Bonferroni technique were
done to investigate differences between levels of variables revealed as significant in the
omnibus ANOVA.
Frequency Effects

There was a main effect for frequency in the comprehensibility data. F (2. 24):
50.356. p<.001. Figure 7 shows that as the number features increased. the perceived

comprehensibility ratings decreased. This effect was large. ([3,): .808. and indicated that

105

 

over 80% of the total effect was accounted for by the variable itself. There was no
frequency-compilation interaction. F (6. 24): .600. [F .195. indicating that the frequency
effect was consistent across all four stimulus compilations.

Figure 7

Average C omprehensibility Scores Elicited from Stimuli Obtained in Relation to the

Frequency and Salience of AAE Features (N=16)

 

Salience
— Low
- - - High

01
l

A
l

 

Average Comprehensibility Ratings
0:
l

 

 

 

.\._
.‘-..“
2.—
1—
l I T
0 1 3

Number of Features

Comprehensibility Rating Scale: 1: Very difficult to understand. 5: Very easy to

understand

106

 

A paired t-test was done comparing the stimuli associated with the different
frequency levels. To control for Type I error. the alpha was adjusted to .025 using the
Bonferroni technique. The comparisons revealed that stimuli containing 3 features were
rated significantly less comprehensible than stimuli containing 1 feature. t(3 l) = 3.215.
p<.001. Stimuli containing 1 feature were significantly less comprehensible than those
containing no features. t(3 l )= 8.530. p<.001. The effect size of the 0-1 difference (d=
1.51) was much larger than the effect of the 1-3 difference (d= .57). This finding was
similar to the frequency effects in the detectability ratings. yet magnified. The inclusion
of one dialectal feature contrasted with no features apparently had a more extreme impact
on the SLPs perceptions of comprehensibility than did the inclusion of three features
contrasted with one feature. The effect from 0-1 feature may indicate the listeners“
awareness of AAE presence in contrast to AAE absence. The effect from 1-3 features.
may reflect less awareness of AAE presence and reflect awareness of increasing use of
AAE.

Salience Effects

There was a significant main effect for salience. F (1.12): 35.593. p<.001. Figure
8 presents these results graphically. Those features classified as less salient elicited lower
comprehensibility ratings than those features classified as more salient. ((47): 5.51 l_.
p<.001. This effect was large. 1131,: .748. and indicated that almost three-quarters of the
overall effect was accounted for by the variable itself. The salience effect did not
significantly differ across the four different stimulus compilations. F (3. 12)= .926. [F

.458.

107

 

Frequency by Sal ience Interaction

Unlike the detectability ratings. the comprehensibility ratings revealed a
significant frequency-salience interaction effect. F ( 2. 24): 4.926. p= .016. This
interaction may be viewed graphically in Figure 8. To analyze this interaction. paired t-
tests were used to make the following comparisons: pair 1) high salience/zero features
with low salience/zero features; pair 2) high salience/one feature with low salience/one
feature; and pair 3‘) high salience/three features with low salience/three features. Type I
error was controlled using the Bonferroni-adjusted alpha of .02. There were no
Significant differences between the components of pair 1. t(15_)= 1.861. p= .083.
indicating that the mere opporttutity for high or low salience features was not enough to
generate a salience effect. In pair 2. a salience effect was elicited when one AAE feature
was included. t(15)= 4.472. p<.001. Specifically. the higher salience features generated
lower ratings of comprehensibility. The effect of this difference was large. (1: 1.12. In
pair 3, the salience effect was maintained when three AAE features were included. t(15)=
3503. p=.003. While the effect size associated with three features was still considered
large, d= .88. it was smaller than the effect elicited when one feature was included (d=
1.12),
Sumn'lanv of C omprehensihility Results

The data associated with the perception ofcomprehensibility yielded the
fOIIOWing conclusions: (a) the Anglo-European American SLP listenersjudged sentences
to be less comprehensible to the general public when they included a greater number of
AAE features. and (b) the listeners judged the high salient features as having a greater

unpaCt on comprehensibility than the low salience features. In addition to these main

108

 

effects. there was an interaction between frequency and salience. Specifically. as the
number of AAE features increased. the ones with higher perceptual salience had a greater
impact on perceptions of comprehensibility than the ones with lower perceptual salience.
l/l’ritten Comments Regarding Study

After completing all of the ratings. the participants were asked to write any
“feelings. comments. or questions about the study.” Six of the sixteen (37.5%) did so.

Two participants commented about the tasks of rating detectability and
comprehensibility. One of these participants stated. “Detectability is a very different
question. because someone can be very understandable. but also have a very noticeable
accent.” She also commented that “consonants are not the only things that influence
detectability—there is something about the vowels that carry the accent.” The other
person commented that “final consonant deletion is difficult to understand anytime.”

The other participants commented that they were not accustomed to viewing the
features included in the study as dialectal. Rather they were apt to label such features as
Phonological disorders. One commented. “It was a different experience to think about it
as ‘African American English’ and not 'we’ve got to enroll this kid!” Another
PaniCipant commented that. “It is somewhat difficult to separate/rate how noticeable the

dlalect is from what. in my students. I would consider an “articulation error’.”

109

CHAPTER IV
DISCUSSION
This section begins with a discussion of the conclusions pertaining to the main
research questions that were informed by the dialect detectability and comprehensibility
data. It continues with a discussion of the observations related to the methodological
variables of the study. Next. it addresses the implications of the study's findings for
theory. applied research. and clinical practices. It then concludes by outlining areas for
future research.
Gradient Perception ofAAE Dialect by SLPs
The purpose of this dissertation was to better understand how AAE use is
perceived among Anglo-European American SLPs. Their perceptions were of interest
because of their high degree of professional power in determining who is a normal or
disordered speaker. The two experimental independent variables.fiequency and
Perceptual salience. were investigated as factors that could influence perceptions of
dialect detectability and comprehensibility in SLP listeners relatively unfamiliar with
AAE.
Frequency of Dialect Features
The data from this study supported the hypothesis that an increased number of
AAE dialect features present in an utterance will elicit increased ratings of dialect
detectability and decreased ratings of comprehensibility from non-AAE speaking SLPs.
In Other words. if many AAE features (of any type) are used within sentences and the
person‘s speech is evaluated by an SLP with little exposure to the dialect. the speaker will

likely be identified as a highly detectable AAE speaker with compromised

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comprehensibility in the general population. The threshold for a significant change of
either comprehensibility or dialect detectability ratings. however. was just one feature.
That is. it only took one feature in an eight-word sentence to increase dialect detectability
ratings and decrease the perceived comprehensibility of the sentence. Although. the
subsequent inclusion oftwo additional dialect features did change both ratings. the
change from one feature to three features did not affect scores as much as the change
from zero features to one such feature. Apparently. all that was needed for the SLPs in
the study to detect that the speaker spoke AAE was one phonological feature. regardless
of the type of feature used. This shift in categorization from a “non-AAE user“ to “AAE
user” created the greatest change in ratings. As additional features were added. no shift
in categorization was needed. The speaker was perceived to be on the continuum of AAE
use from that point on.

The same type of threshold effect occurred with the comprehensibility ratings.
One feature was enough to significantly decrease the SLPs‘ projections of how
understandable the person would be to the general population. We may conclude.
therefore. that presence is of greater weight thanfrequencr’.
Perceptual Sal ience of Dialect Features

Based upon the expectations of Steriade (2004). perceptual salience was expected
to influence comprehensibility and dialect detectability ratings. Thus. the categorization
of the AAE phonological patterns examined was based on that very factor. However.
perceptual salience did not appear to influence the ratings of both tasks in the same way.

Comprehensil)ilit)y'zulgrrzents. Perceptual salience appeared to accurately classify

the features for the comprehensibility ratings (i.e.. the group of features with high

111

perceptual salience elicited lower comprehensibility ratings than the features with low
perceptual salience). However. given that the sentences were rated as a group for
comprehensibility. a true feature-by-feature comparison could not be made. F urthemtore.
the features were already grouped according to perceptual salience in the very stimuli that
were rated. The listeners were not free in this task to base their judgments on other
groupings. as they were in rating the dialect detectability task. Nevertheless. the data
suggest that SLPs may have considered the perceptual salience of the features in
detemtining which sentences would be comprehensible to the general population and
which ones would not. One particular construct in the methodology could have aided
these results—the orthographic transcription of the auditory stimuli.

In rating the projected comprehensibility of the sentences. the SLPs were
presented with an orthographic transcription which corresponded to the auditory stimuli.
This type of task is different from other studies that examined comprehensibility. For
instance. the studies that examined perception of nonnative accents. (e. g.. Derwing &
Munro. 1997; Munro & Derwing. 1995a. 1995b). did not provide an orthographic
representation of the auditory stimuli. However. SLP listeners in this study may have
been less linguistically naive than the listeners from the general population who
participated in second language accent perception studies. SLPs have specific training in
phonetics and phonology that the participants in the other studies would not have had.
This additional knowledge of the SLPs created several questions regarding what they
would base their judgments on. Would the SLPs recall and use their knowledge about
phonetics and phonology in making their ratings. or would they not? If they did use such

knowledge to make their judgments. on what phonetic target would they base their

decisions? Due to these questions. some added stability was required. However. the
stability that was added was not unlike the inherent stability in the SLPs’ actual practice.
Specifically. it was similar to the relational analysis (comparing the client's productions
to an intended target or model) that many of them would use in assessing the
phonological patterns of their clients.

The inclusion of the orthographic transcription created a need for an additional
modification from the procedures used in the majority of second language accent
perception studies. That is. the SLPs were not asked to base their comprehensibility
ratings on whether or not they themselves understood the sentence. Instead. they were
instructed to base theirjudgments on whether or not the people speaking would be
understandable to others in the general population. Presumably. this modification
required each SLP to base her judgments on a self-determined paradigm—perhaps the
very paradigm that she would have used with her clients. In fact. one participant
remarked that “final consonant deletion is always difficult to understand." Such a
statement appears to reveal a portion of the paradigm on which she based on her ratings
(i.e.. If the client omits a final consonant. then the comprehensibility rating should be
decreased). Indeed. some clinical researchers have developed guidelines concerning
what phonological patterns are likely to impact a given client’s intelligibility in the
general population. Such guidelines may have been instrumental in assisting some of
participants with the creation of their rating paradigm.

One popular phonological disorders assessment. The Hodson Assessment of
Phonological Processes-3 (’HAPP-3; Hodson. 2004) weights final consonant deletions

more heavily than other phonological patterns due to its expected negative impact on

 

intelligibility. It is possible that paradigms similar to the one used in the HAPP-3 were
used in making the comprehensibility judgments.

Dialect detectability jm‘lgments. The categories of perceptual salience did not
explain the dialect detectability scores as consistently as they did the comprehensibility
scores. The high salience features did elicit higher dialect detectability scores than the
low salience features in most cases. However. when the dialect detectability ratings were
examined post hoc for each feature individually. their ranking suggested that the
perceptual salience did not entirely explain the ratings (refer to Table 14. p. 103). In fact.
the feature that received the lowest perceptual salience ratings in the preliminary
experiment involving nonsyrnbolic syllables (f/th substitution). elicited the second
highest dialect detectability ratings in the main study. This discrepancy was allowed due
to the fact that the features were not pre-grouped as they had been in the
comprehensibility task.

The data from the dialect detectability task were gathered differently from the
comprehensibility task. Because of this difference. the participants were free to group the
dialect features as they wished. Such a difference limits the extent to which the data
generated by the two tasks can be compared. However. the tasks were also inherently
different perceptual judgments. This point was acknowledged in writing by a participant
after completing the experiment. "Detectability is a very different question. because
someone can be very understandable. but also have a very noticeable accent.”

One difference between the two tasks concerns the prerequisite knowledge of the
SLPs in making informed ratings. While comprehensibility could be determined without

any specific knowledge of common AAE features. the dialect detectability task required

114

 

some degree of awareness ofphonological features associated with the AAE dialect. In
fact. one participant commented on her own lack of AAE phonological awareness. "It
was a different experience to think about it as ‘African American English“ and not ‘we've
got to enroll this kid!”9

The SLP participants were selected based on their lack of familiarity with AAE.
They indicated that they did not speak AAE themselves. and they did not converse
regularly with a person who spoke AAE. If they only heard AAE spoken infrequently.
then those features used most commonly by AAE speakers in the general population may
have been rated higher in detectability. In the terms of Preston's (1996a) work. they may
have rated those utterances as highly AAE detectable that included features highly
available to them and for which they could readily cite as characteristically AAE (i.e.
detail). Irvine (2001) described features such as these as iconic. For example. if the
SLPs associated the f/th substitution pattern as particularly characteristic of AAE. then
this one feature might elicit higher detectability ratings. regardless of its perceptual
salience in nonsyrnbolic units.

If Steriade’s (2004) expectation that less salient features will be used more
frequently by speakers in all languages and dialects is correct. then the least perceptually
salient features may have higher iconicity by virtue of their ubiquitous use in the
language of AAE speakers. In this case. the participants of the study should have rated
the low salience features as having higher dialect detectability in all cases.

This scenario did not manifest either. As a group. the high salience features often
elicited significantly higher dialect detectability ratings than the low salience features. In

fact. the final consonant deletion of /k/ received the highest mean D-score. indicating that

115

 

 

this feature created the greatest increase in dialect detectability ratings of all the featru‘es
studied. Final consonant deletion of /k/ was also the most highly perceptually salient.
Additionally. it was identified less frequently than th substitutions and cluster reduction
in the Craig and colleagues (2003) study. Stockman (In Press) further identified that final
/k/ deletion occurred significantly less frequently than final /t/ deletion in a corpus of
AAE speaking children.

It appears that a combination of the two factors occurred: (a) ubiquitous use by
AAE speakers and (b) perceptual salience. That is. those features that were so commonly
used by AAE speakers received inflated detectability ratings due to their heightened
iconicity with the dialect for the listeners. In this case. the f/th substitution pattern as the
highest ranked of the low salience features would be the most iconic (second highest
mean D scores of all of the features). In contrast. the perceptual salience of the deleted
final /k/ was high enough that it didn‘t matter if the SLPs associated it with AAE or not.
Its highly perceived use was enough to increase dialect detectability ratings and produce
an overall perceptual salience effect for much of the dialect detectability data.

As for the three features receiving the lowest dialect detectability ratings. they all
involved a final /t/ (t/th. F Dt. and F Rt). In the final position of words it is produced
variable in many dialects of English. These variations include reduction to a glottal stop
or omitted entirely in many dialects of English. even those considered standard (Guy.
1980; Stockman. In Press). It is regularly deleted in the final position of words when
occurring in a consonant cluster (i.e. fast-9 lfzes/) in standard varieties of American
English. particularly when a consonant follows. as it did in this study. Taking these facts

into account. it is understandable that features involving final /t/ would receive lower

116

 

dialect detectability ratings than other features involving other consonants. It is possible
that when a feature involved a final /t/. the listeners had a wider range of acceptance due
to the great variability of final /t/ production among speakers of many dialects ofEnglish.

Sana-may ofperceptual salience contribution. This study did not ask the SLPs
how much they associated each phonological pattern with the AAE dialect. However. the
possibility exists that the listeners might have associated some of the features as
characteristically AAE. and those features may have influenced their decisions about
AAE detectability more than others regarding a sentence’s AAE detectability. This
seemed to contrast with the rating of comprehensibility. As one of the participants
implied in her written comment. the listeners might have considered some features as
contributing significantly to the detectability of the dialect without having a negative
impact on comprehensibility in the general population. Conversely. some features may
be deemed to affect comprehensibility in the general population but not be iconic enough
to contribute to the dialect‘s detectability.

In Figure 2. p.11. a model was generated to illustrate how remarks from the
general population about how intelligible and noticeable a person is may contribute to the
overall perception of dialect density. In this model the two factors existed along a single
dimension. or plane. If a speaker was unintelligible to the listener and spoke with a
highly noticeable dialect. then he/she likely would be characterized as speaking with a
“very thick" accent/dialect. Conversely. if the speaker was highly intelligible and spoke
with a dialect that was not noticeable to the listener. then the speaker likely would be
identified as speaking no accent/dialect. Figrue 8 reframes this model with the

terminology used most frequently in this study. For example. very intelligible and

117

 

unintelligible correspond to high comprehensibility and low comprehensibility.
respectively. Likewise. not noticeable and very noticeable correspond to low
detectabilitv and high detectability. The scale was converted from one reflecting the
statements made from members of the general population to one reflecting the general
concept of perceptual dialect density.

Figure 8

Original Single Dimensional Model of Perceived Dialect Density

 

High C omprehensibility Low Comprehensibility
Low Detectability High Detectability

0

Low Density High Density

By conflating judgments of comprehensibility and dialect detectability. this single
dimensional model did not take into account the inherent differences between the two
judgments that were reflected in both the quantitative data and the written comments in
this study. Dialect detectability and comprehensibility may not operate along the same
dimension. Instead. the factors of comprehensibility and detectability may operate along
two intersecting dimensions. as depicted in Figure 9. In this revised model. the
perception of dialect is multidimensional. capturing an additional layer of complexity.
The revised. multi-dimensional model shows that both dialect detectability and
comprehensibility contribute to the perception of dialect indicated in statements
commonly heard from members of the general population. Both comprehensibility and

detectability function as separate factors in the gradient perception of dialect.

118

 

Figure 9
Multidimensional Model of Perceived. Dialect Density
The four quadrants represent perceptual qualifiers that might be used by listeners to

categorize speakers who have the given coordinates of comprehensibility and dialect
detectability.

 

High Comprehensibility

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Accent/Dialect Unidentifiable Accent/Dialect Strongly Identifiable
AND Easy to Understand BUT Easy to Understand
Example: Example:

Standard English speaker “without " US Bostonian
an identifiable regional/etlmic 1 Standard English Dialect
accent/dialect
Low Detectability High Detectability
. _ . Accent/Dialect Strongly Identifiable
AccentiDralect Unrdentrfiable BUT Difficult to Understand
AND Difficult to Understand Etample:
Example: African A merican English Dialect
Ocracoke English Dialectfrom
North Carolina ’s Outer Banks

 

 

 

 

 

 

 

Low Comprehensibility

 

 

 

The revised model creates four quadrants that correspond to categories in which a
speaker might qualify according to the degree that his/her dialect is deemed
comprehensible and detectable. Ifa person’s dialect is both highly detectable and
incomprehensible to a listener. then he/she is likely to be perceived as a nonstandard
speaker with a very strong. or heavy dialect. Such is the case for speakers of socially
stigmatized dialects (e. g. African American English) who use a significant number of
features that are both highly identifiable with the dialect and perceptually salient.
Conversely. if the person's dialect is not highly detectable and highly comprehensible.
then he/she may be perceived as a stamlard English speaker "without" an accent/dialect.

Alternatively. a person may fall into two other categories. One category corresponds to

119

 

speakers with a highly detectable but comprehensible dialect. This category might
include speakers of distinctive dialects that either are highly familiar to the listener (e. g..
regional dialects from the listener‘s own geographical area or highly prestigious. yet
uncommon dialects (e.g.. Brahmin dialect of Boston). Conversely. a speaker may also be
judged to be incomprehensible. but speak without a great degree of dialect detectability.
Perhaps the dialect is one to which the listener has never been exposed (e. g.. Ocracoke
English from the Outer Banks ofNorth Carolina). The use of these less identifiable
dialect features could cause the perceptual judgments to be weighted more in the area of

comprehensibility limitations and less in the area of dialect detectability.

This model helps to explain the interrelationship between two perceptual variables

on which global judgments about a person‘s dialect are based. However. other factors
may also influence these gestalt dialect judgments. Therefore. additional research should
be done to identify whether other factors contribute to the holistic notion of dialect
perception.
Methodological Variables

The methodological factors. compilation (4 levels) and speaker (2 levels). did not
create drastically different results but did indicate that there is some variability inherent
in the process of perceptually rating dialect. The variability present in the four stimulus
compilations and the two different speakers will be discussed.
Differences Among Compilations

As described earlier. the compilations included the variables of speaker order and
task order. Differences among the compilations were present only in the detectability

ratings. These ratings did not reflect a consistent main effect for speaker order or task

A rE'L

 

order. Instead there seemed to be an interaction between these two variables. In essence.
the order of the tasks created differences only when speaker I was first; speaker order
created differences only when the comprehensibility task was first.

Two factors. other than the intended variables inherent in the compilation C Ds.
could have caused these compilation differences: (a) unintended clustering of highly
salient AAE phonological patterns in the first few stimuli on some compilations. and (b)
unintended clustering of inexperienced or highly experienced SLPs in one or more
compilations.

Niedzielski‘s (1999) observations suggested that individuals may make
conclusions about an entire sample based on their initial experiences with a speaker.
Therefore. the initial five stimulus items on each of the compilations were reviewed for
unintended clustering of either high or low numbers of AAE features. No clear clustering
of frequencies could account for the compilation differences on the compilations.

The second possible factor. experience level of the four groups of SLPs. was also
examined. In this analysis. the experience levels of the SLPs were analyzed according to
compilations. The years experience for the SLPs included in the different compilation

groups are presented in Table 15.

l m. m—.
.

 

Table 15

Years Experience as SLP for Participants Organized by Stimulus Compilation (N=16)

 

Compilation 1 2 3 4

 

ID# 15g9i13 21611014 3 711115418112j16
Years 629|9j33 7i18 5 21 1410(13 4 317,516
Experience l ' . l J l

 

 

 

 

 

 

 

Descriptive M: 19.25. M= 12.75. M= 10.25. M= 5.25.
Statistics SD = 13.72. SD = 7.93 SD = 4.50 SD = 1.71
Range: 27 Range2 16 Range: 10 Range= 4

 

 

 

 

 

 

 

The average experience values for the participants in each compilation were
tested for differences using the nonparametric Kruskal-Wallis H Test for multiple groups.
Although the average experience level in group 4 appeared significantly lower than the
average experience level in group 1 (cf.. M= 27 and M: 4 for compilations 1 and 4.
respectively). the difference was not statistically significant at the .05 level. Kruskal-
Wallis H (3) = 4.669. p= .198. However. even if the difference in experience levels
observed among the compilations were significantly different. the differences could not
consistently account for the compilation differences. While such a difference might have
explained why compilations 1 and 4 were placed in separate detectability groups based
on the participants. ratings. it would not explain why compilation 4 (the least experienced
group) would have been so similar to compilation 2 (the second most experienced group)
as to have had the same mean dialect detectability rating (M=3.22).

As Flege and Fletcher (1992) described. there are many factors that may influence
perceptual ratings of second language accent detectability. Perhaps the rating of dialect is

r10 different in this sense. It is possible that several factors combined to affect the ratings.

 

 

or there are factors inherent in the compilations that have not yet been identified in
research on cross-dialect detectability. Further examination of the factors influencing
dialect detectability needs to be done before a clear explanation of the differences among
the compilations can be developed.
Differences Between Speakers

There were some inconsistent differences among the dialect detectability ratings
elicited by the speakers. These differences were not qualitative. except in the high
detectability group when one speaker elicited a salience effect and the other did not.
Such slight speaker differences were to be expected. Use of subtle segmental and
suprasegrnental speech characteristics may vary widely not only between different
speakers but within the same speaker. This individual variability is complicated by the
fact that listeners may also vary in how such characteristics may affect perception.

Theoretical and Research Implications

The findings of this study have implications for five specific theoretical and
research paradigms: (a) Steven‘s Law and general sensory perception. (b) Johannson‘s
(1978) model of nonnative accent feature-specific perceptual weighting. (c) Steriade’s
(2004) P-map model, (d) Prototype-based models of speech perception. and (e)
Washington and C rai g‘s (1994. 1998) process for the calculation of dialect rate (density).
Implications for these paradigms will be discussed in the following paragraphs.
Steven 's Law and General Senson Perception

The notion that perception of a stimulus will increase as its magnitude increases
has been accepted in general perceptual theory for many years. It was articulated by

Stevens (195 7) in Steven‘s Law. which expressed that the degree to which sensory

 

stimulation is perceived is directly related to the magnitude of the sensory input. Such
perception is represented with the formula: S = kl 8. The S represents the intensity of the

stimulus perceived. The k represents a constant. and 1 represents the magnitude of the
stimulus. The exponent a corresponds to the unique effects that the wide array of sensory
experiences have on the process of perception. The exponent is specific to different types
of stimuli.

The perception of a dialect may be similar to other sensory perceptions. and
therefore. may be informed by the principles on which Steven‘s Law (1957) is based.
Framed in accordance with the variables included in this study. S represents perceived
dialect density. 1 represents thefi'eauencv variable. and the exponent a may represent the
different types of dialect features involved. Considering Steven‘s Law. it is not
surprising that the variable that most directly communicated the magnitude of the dialect.
frequency. had such large effects on the ratings of both dialect detectability and
comprehensibility. The reduced. but present effect of the type of features used in this
study indicates that the values of the exponent a in this specific perceptual experience
may be slightly only slightly different for the different dialect features included.
Johannson 's F circtors for Non-m'itive Accent Feature Perceptibility

Johannson (1978) suggested that two factors influence the degree to which a
feature contributes to the detection of a second language accent: (a) the degree to which
it impacts speech intelligibility. and (b) the degree to which it is “irritating” to the
listener. Since Johannson was interested in nonnative speech patterns. he discussed his
findings in terms of the types of mistakes made by speakers in speaking a language that

was not their native language. Dialect features may be analogous to the second language

124

 

 

mistakes addressed by Johannson. In particular. both are variations from what may be
familiar to listeners. While particular AAE dialect features may be irritating to some
listeners. the features may not need to be irritating to increase the perception ofdialect
density. If the factor of feature irritation mentioned by Johannson is analogous or related
in some way to dialect detectability. then the data in this study may support the general
idea of Johannson’s argument. That is that awareness of a dialect feature may be a factor
in detemrining the degree to which the dialect will be perceived on a scale of density.

The model proposed by the investigator (see Figure 9) to explain the results of
this study was similar to Johannson’s (1978) model in the sense that there are two factors
that influence the perception of language variation. One is the degree to which the
variety is understandable to the listener. and the other relates to the degree to which a
feature calls attention to itself. In this way. Johannson’s model is supported by the data
in this study. However. with respect to dialects. Johannson’s factor irritation may be
replaced with the broader factor dialect detectability, which may be affected in part by
listener irritation. dialect iconicity. or perceptual salience.
Steriade 's P-Map

Steriade (2004) asserted that the features with low perceptual salience may be
used less frequently by speakers than the features with high perceptual salience. The
preliminary study involving perceptual salience of AAE phonological patterns in
nonsymbolic units appeared to support Steriade’s P-map expectations. This was verified
by the phonological patterns documented from language samples of school-aged AAE-
speaking children in Craig and colleagues (2003) study. The features that received the

lowest ratings of perceptual salience in the preliminary study were the same ones used

125

 

most often by school-aged AAE-speaking children in the work from Craig and colleagues
(2003). Stated simply. low perceptual salience results in increased use. Increased use of
a feature by dialect speakers is likely to cause increased association with the dialect. or
increased feature iconicity. and iconicity may be but one factor in dialect perception
(Irvine. 2001).

The perceptual salience classification obtained from the preliminary study
described in this dissertation was derived from Steriade‘s (2004) suggestions concerning
the P-map. As described previously the P-map is a representation of the perceptual
salience ofcontrasting phonological features. Such perceptual salience did appear to
influence comprehensibility ratings. Steriade argues that speakers avoid perceptually
salient phonological patterns by seeking the alternation which changes the underlying
representation‘s (UR) form minimally. This is done to comply with the phonotactics of
the language/dialect being spoken. while still maintaining the closest approximation to
the UR as possible. As alternations become perceptually distinct from the UR. the signal
may be compromised for wider and wider audiences. Since those very features that were
ranked high in perceptual salience were also deemed to have the greatest impact on
perceptions of comprehensibility. the comprehensibility data are in accordance with the.
expectations expressed by Steriade (2004). F urthemrore. many of these high salience
features did not appear to be identified strongly with AAE as evidenced by their

contribution to the dialect detectability results. By this interpretation of the data. the

 

dialect detectability results may also support Steriade’s assertion that features that are

highly perceptually salient will be used less frequently by speakers. This may explain

why many of the high salience features were not associated with the dialect by listeners
who were relatively inexperienced with the dialect.
Prototrpe-Based Models of Speech Perception

Prototype-based theories of speech perception maintain that listeners match
auditory input to an internal prototype. For example. Kuhl’s (1994) Native Language
Magnet Theory states that perception of speech input is based on a person’s own
phonological system. Phonernes are perceived in categories that are developed from the
listener‘s own linguistic experiences. lfa listener hears a phonetic feature that falls
within the listener‘s perceptual boundaries of a particular phonemic prototype. then the
feature will be perceived as that phoneme. This is the case even when the speaker
intended a different phoneme to be perceived.

This study did not allow for such phonemic misclassifications to affect
intelligibility. Because the orthographic representation of the sentence was included. the
listeners knew the intended phonemic targets. However. such misclassification could
have affected the degree of perceived comprehensibility. If the perceived phonemes did
not match those expected from the orthographic transcription. then it is reasonable to
assume that the listener would have classified the speaker as having low
comprehensibility in the general population. Such findings would support Kuhl‘s (1994)
Native Language Magnet Theory.

Trends in the data also support the Fuzzy Logic Model of Speech Perception
(F LMSP; Massaro. 1988). F LMSP suggests that when speech is heard. the listener will
go through three operations of phonemic decoding. First. a feature is either noticed or

not noticed. If noticed. then the feature is matched with prototypes stored in the listener‘s

 

 

memory. Finally. the feature is classified according to its similarity with the stored
prototypes. Such classification is based uponfiiz; i (i.e.. approximate) values assigned
subconsciously by the listeners (e. g.. 0= definitely not a match. 1= definitely a match. .5=
ambiguous). The F LMSP would predict that the most perceptually salient phonological
patterns would have received the lowest fuzzy values. That is. they would be the furthest
away from matching the prototypes that corresponded to what was expected from the
orthographic representations. Such mismatched multiplied by the frequency of
unfamiliar or unexpected dialect features would further decrease the fuzzy values for the
entire sentence. In the comprehensibility data. there were both main effects for frequency
and main effects for salience. F urthermore. the interaction indicated a greater cumulative
effect as high salience features were added than when low salience features were added.
These findings support the F LMSP.
Implications for Research Methodologr

There are several ways in which dialect density has been calculated in child
language research (Oetting and McDonald 2002). The calculation usually entails
summing the number of dialect features and dividing by some unit of language; however.
the formula has rarely been used in conjunction with perceptual evidence. The study‘s
data support the notion that the number of phonological features affects perception of
AAE detectability and comprehensibility. However. the fomrula for dialect density
calculation may be too simplistic to adequately represent cross-dialectal perceptual
experiences.

The calculation of dialect density is analogous to the calculation of physical

density. including the assumption that each component of the formula is equally

128

 

weighted. Dialect density calculations do not typically consider the types of features
involved. This study"s data suggest that the specific types of features occurring in a
speech sample should be considered before an accurate representation of cross-dialectal
perception is approximated.

The data also revealed another limitation on the use of dialect density calculation
pertaining to dialect perception. That is. the dialect density calculation assumes each
additional dialect feature will affect perceptual judgments equally. Throughout the data
in this study. the inclusion of one feature consistently elicited greater differences over no
features than three features did over just one feature. In this sense. counting each feature
equally may assume that each feature is of equal weight. which may not be the case
perceptually.

The use ofdialect density calculation methods may be adequate in categorizing
participants for the purposes of expressive language data analysis; however. such
categorization may be limited to quantifying dialectal expression. Models used to make
inferences regarding the perception of dialect in speakers by listeners. who do not speak
the dialect. should consider the specific types of features involved in the person’s speech
as well as the fact that the relationship between the dialect density average and listener
perception may not be linear.

Practical Implications

The implications of this study are relevant for: (a) SLPs currently practicing in the

public schools. (b) academic programs devoted to training SLPs. and (c) SLPs engaging

in procedures of accent modification.

129

 

Implicationsfor SLPs in the Public Schools

Many aspects of the dissertation‘s study were designed to replicate in a
controlled. experimental way the circumstances that might occur if an actual AAE-
speaking child were evaluated by an SLP with little exposure to AAE. The study is
limited in its ability to predict the effects of such a scenario for two reasons: (a) It was not
naturalistic. and (b) it did not involve children. In spite of these limitations. the findings
may illuminate some of the possible outcomes of such a scenario.

The first practical question to be answered is “Who would have been labeled as
speech disordered by SLPs who were using the traditional interpretation of Van Riper’s
(1978) definition?“ Van Riper’s definition of speech disordered includes speech that:
(a) calls attention to itself. (b) interferes with communication. or (c) places an emotional
burden on the speaker. While the last criterion could not be inferred from the data in this
study. the first two criteria may be directly informed. The detectability ratings
correspond to criterion a. and the comprehensibility ratings correspond to criterion b.

The data suggest that speakers who include a greater number of AAE
phonological features will be deemed less comprehensible to the general population and
more detectable as an AAE-speaker. The data also suggest that speakers who include
features with greater perceptual salience will be judged less comprehensible to the
general population —— specifically. deletion of /k/. deletion of /t/. and t/th substitution.
Furthermore. the findings imply that those individuals will likely be identified as using
detectable AAE who: (a) use features that are so ubiquitous in AAE speakers that their
use is associated strongly with AAE (e.g. f/th substitution) or (b) use features that are

extremely perceptually salient (e.g. k-deletion).

While judgments of SLPs practicing in the public schools may be infomred by the
data in this study. it is unclear what actions would be most commonly taken in response
to the judgments. Such actions are detemrined by how the SLPs interpret the definition of
speech disordered. If the SLP interprets the Van Riper (1978) definition traditionally.
then the decision of the SLP is clear. Children identified as either less comprehensible to
the general population and/or identified as using a highly detectable form of AAE are to
be identified as speech disordered.

Alternatively. Payne and Taylor (2005) suggested that SLPs with a more accurate
perspective toward communication might interpret the traditional definition of speech
disorder with the understanding that all communication is socio-culturally based.
Individuals are speech disordered. whose speech draws attention to itself or is difficult to
understand within the communityfi'om r-vhich they learned their language (i.e.. their home
speech community). A non-AAE-speaking SLP is not from the speech community of
his/her AAE-speaking client. Therefore. they must observe the client within the home
speech community. interview the child‘s primary caregivers. and/or have an
understanding of the normal dialectal patterns common in the speech community.

With this basic understanding. the SLP will likely use the evidence of the highly
detectable AAE features to further examine the adequacy of the child‘s cormnunication in
relation to his/her speech community. Two of the three most detectable features in this
study were identified by Craig and colleagues (2003) to be the features most frequently
used by school-aged AAE-speaking children (th substitutions and final cluster reduction).
This suggests that AAE-speakers commonly use features that will be detected and

identified as characteristically AAE by SLPs. who are relatively unfamiliar with the

131

‘.

 

dialect. If the SLP interprets the Van Riper definition in a socio-cultural context. as
Payne and Taylor suggested. then the child will likely receive less biased assessment
procedures based on the perceptual judgments used as dependent variables in this study.
Implicationsfor Instructors of SLPs

These findings. in light of the Payne and Taylor (2005) suggestion, place heavy
responsibility on the institutions providing academic and clinical training to prospective
SLPs. The institutions must instill a socio-cultural view of communication in their
students from the very beginning to prevent misdiagnoses with minority dialect-speaking
children. Such a view requires all discussion of communication. both normal and
disordered. to be in a socio-cultural context. Although such education is now required by
ASHA (1999). there are few guidelines in place for this education. In fact. Stockman.
Boult. and Robinson (In Preparation) discovered that a majority of general course
instructors in communication disorders programs reported that they spend a minimum
amount of time discussing multicultural issues in their courses. This was the case. even
though a majority identified themselves as strongly committed to infusing multicultural
issues into their courses.
Implicationsfor SLPs Engaging in Accent Modification

The findings ofthis study also have implications for those SLPs engaging in
accent modification. If an AAE-speaking individual seeks to speak a dialect that is closer
to Standard American English in particular social situations. then the phonological
features that would be of greatest detectability would be worthy of targeting. Such
features might include: (a) final k-deletion. (b) f/th substitution. and (c) sk-cluster

reduction. Alternatively. a general goal to reduce the overall frequency of AAE feature

use would be affective in increasing the perception ofcomprehensibility and decreasing
the perception of dialect detectability. However. as addressed by ASHA (l983b). accent
modification must be done with contextually-based goals. That is. the situations in which
a standard dialect would be desired are addressed along with the understanding that the
standard dialect should not replace all communicative styles.
Study Limitations and Future Research

The present study is one of the first studies to examine factors that might
influence how SLPs perceive the AAE dialect. Much more research needs to be
conducted to further explain how SLPs perceive other nonmainstream dialects. The
discussion of study limitations and future research is organized in the following
categories: (a) listener variation. (lb) stimulus variation. (c) task variation. and (d) speaker
variation.
Listener l«"ariation

The listeners in the study were Anglo-European American SLPs from Michigan
who had little exposure to AAE. It is unknown how well the results would generalize to
other listeners. such as AAE speaking SLPs. non-AAE speaking SLPs who are familiar
with AAE. or SLPs who speak other stigmatized varieties of English. Such research is
needed to determine how personal experience with different language varieties influences
perceptual judgments ofdialect detectability and comprehensibility.
Stimulus Variation

The stimuli involved in this study included just a few commonly cited AAE

features in the final position of words. Further research should focus on how other

133

 

dialect patterns impact perceptual judgments ofcomprehensibility and dialect
detectability.

Furthermore. studies involving videotaped stimuli may yield different results. For
example. the t/th substitution pattern wasjudged to have high perceptual salience in
nonsymbolic syllables in the preliminary experiment compared to the f/th substitution.
The relative perceptual salience of these features may differ with videotaped stimuli.

The t/th substitution. for example. involves only tongue tip variation and has subtle visual
differences. The f/th substitution. on the other hand. involves variation of lip movement.
which creates a more visually salient difference. Given the fact that SLPs generally
assess their clients face to face. this would be an important addition to subsequent studies
to determine if different perceptual ratings might vary across visual and auditory stimulus
presentations.

Task Variation

The tasks involved were constructed based on testimonials from nonlinguists in
the general population (Alvarez and Kolker. 1986; Niedzielski & Preston. 2004). These
lay interviews included statements about dialect comprehensibility and detectability
juxtaposed with perceptual statements about dialect density. It was assumed that SLPs
would have similar thoughts about dialects. but more empirical evidence of this is
needed. SLPs are different from the general population in that they receive a significant
amount of information regarding phonological patterns in their academic and clinical
education and are likely to have experience assessing and treating phonological disorders.
That is. the SLPs had experience analyzing phonology. This is experience that people in

the general population are not likely to have. This experience may have caused the SLPs

134

 

to have heightened rnetalinguistic awareness of the phonological patterns present in each
sentence. Such awareness could indicate that further research with SLPs may be
conducted with more explicit statement of its aims. For example. SLPs could rate
phonological features regarding how highly they associate such features with AAE and.
conversely. how greatly they believe the features impact speech intelligibility in the
general population. Such an explicitly worded task might demystify much of the thought
processes that occurred in this study. In short. what are the folk linguistic theories of
SLPs?

Additional research also should be conducted that examines the comprehensibility
or intelligibility of AAE utterances by the SLPs themselves. rather than the current study
that examined perceptions of how comprehensible others might find the stimuli.
Although the comprehensibility methods satisfied the purposes of the dissertation's
study. the actual difficulty of the SLPs in understanding AAE speech samples is also of
interest. For example. the SLP’s own perceptions of intelligibility may correlate higher
with the likelihood of misdiagnoses than the SLP's perceptions of comprehensibility to
others.

Speaker Variation

The stimuli in this study were spoken by two adult female AAE speakers. It
would have been ideal to include child speakers; however. the speaker's task was judged
to be too difficult for a child to do. Now there is some indication regarding the
perceptual paradigms that may be used in creating perceptual judgments ofdialect
density. The findings of this study may be used to help inform methodology and

interpretation of results in subsequent studies using actual child language samples.

A

 

Conclusions

The data from this study suggest that SLPs will judge speakers as more AAE
detectable and less comprehensible to the general population who use more features in a
linguistic unit. Furthermore. if speakers use features with greater perceptual salience.
then they may be viewed by SLPs as less comprehensible to the general population. At
least two factors appeared to influence the degree to which specific types of features
contributed to ratings of AAE detectability: (a) perceptual salience and (b) feature
iconicity.

The findings support several theoretical and research paradigms. however. they
suggest that the methods for calculating dialect density production may not generalize to
perceptual experiences. F urtherrnore. the findings have several implications for SLPs in
traditional practice and those doing accent modification work. Additionally. the study‘s
findings highlighted the importance of instilling a socio-cultural view ofcornmunication
in SLPs from the beginning of their training.

This study was among the first to examine SLPs“ perceptions of dialect use.

Much more work needs to be done to illuminate the multifaceted puzzle of dialect density
Perception by SLPs and by other professionals with the power to evaluate the

communicative adequacy of people.

APPENDICES

137

Appendix A

Description of Pilot Studv

A bstract

African American students are commonly misdiagnosed as speech-language
impaired by speech-language pathologists (SLPs) in the schools. Although the degree to
which the dialect of African American English (AAE) is noticed in these students by
SLPs could be a key to this disparity. little research on this area has been done. It is
likely that some dialectal features are more noticeable than others.

The purpose ofthis project was to determine which phonological features
associated with AAE were most noticeable to SLP graduate students who do not speak
the dialect. Selected features that are common among AAE speakers were included in
nonsense syllables and paired against a Standard American English equivalent. These
pairs of different sounds were embedded within a list of recordings that included control
pairs that were phonetically similar. The stimuli were recorded by three female speakers
and presented over earphones using a participant—guided Power Point presentation.
Participants rated the perceived difference between the two sounds in the pair on a 4-
point ordinal scale.

The participants rated the following phonological features as the most salient.
respectively: (a) post-vocalic r alternations. (b) t/th substitution. (c) final consonant

deletion. (d) final cluster reduction. and (e) f/th substitution.

 

Appendix A (C ont'd)
Introduction
This pilot study was designed to determine how noticeable selected phonological

features associated with AAE were to SLP graduate students who were unfamiliar with
the AAE dialect. The phonological features were placed in nonsense words to determine
their perceptual salience apart from a linguistic. or meaningful. context. The study
examined the following features (See Table 1):

Table 1

The Phonological Processes and Affected Phonemes Used in the Pilot Study.

 

Phonological Process Affected Phonemes Example

Final Consonant Deletion /t/ and /k/ fat->fa. tack-) ta
Final Cluster Reduction st and sk fast-) fOs or ask-)Os
Final th substitution f/th and t/th with-)wif. wit

Final r alternation vowel/r and deleted r four-) foe. fo

Fourteen participants (13 female and 1 male) listened to 84 pairs of nonsense
words spoken by three different female voices. The final consonant of the first syllable
varied. while the rest ofthe word remained constant (i.e.. da_me. datrne. dafme. datlrrne.
etc.) This syllable structure was used to simulate the phonetic environment that occurred
in the dissertation study stimuli. Some of the pairs contained the same nonsense word
twice (e. g.. datrne datme). and some contained different nonsense words (e.g. dafrne
datrne; see Table 2'). The pairs were blocked by phonological process so that the listeners

heard nonsense pairs associated with only one phonological process at a time.

Appendix A (C ont‘d)
The order of presentation was randomized twice across phonological patterns.
speakers. and token presentation. In essence. there were two separate randomized

orderings.

Table 2

Nonsense words used in the pilot study.

 

F C D C R th sub final r_
datme dastrne dathme dorrne
dakme daskrne dafrne dome
dame dasrne datrne dawrne

The same [me] recording was used for each of the nonsense words within the four
groups. Each of these nonsense words were then paired in the following manner with 1.0
seconds of silence generated between the tokens within the pairs and .5 seconds of
silence generated before each recording (see Table 2):

As the pairs of nonsense words were presented to the participants. the participants
indicated the degree to which they felt the two nonsense words in each pair were similar
or different ( l= exactly the same. 2: almost the same. 3: significantly different. 4=
extremely different). This scale is similar to the one used by Preston (1993) in which
participants were asked to detennine the extent to which various dialects in the US.
differed from their own (I: no difference. 2: slightly different. 3: different. 4:

unintelligibly different). The modification was made to include a forced dichotomy of

140

 

\

 

Appendix A (C ont’d)
same-different.
Results

The ratings for the experimental pairs (i.e.. the ones that were different) were
subtracted from the control pair that represented the Standard English production. Thus.
to test the saliency of final /t/ deletion. the perceptual ratings for the [datme da me] pair
and the [da_me datme] pair were subtracted from the ratings associated with the [datme
datme] control for each participant. This resulted in a perceptual salience rating for each
affected sound under each process. The ratings for each of the two comparison sounds
under each process were then tested for difference using the Wilcoxin Signed Rank Test
and adjusted for Type 1 error using the Bonferroni method. None of the compared
sounds under the phonological processes was found to be statistically significant except
for the th substitution process. In this case. t/th substitution was significantly more
salient than f/th substitution.

The different phonological processes were tested for differences using the
Friedman Test. The results showed that the phonological processes received significantly
different saliency ratings with the following ranking:

Final r processes >>t/th substitution>>Final Consonant Deletion>>Final Cluster
Reduction>>flth substitution.

Post hoe analysis using the Friedman Test revealed that the final r processes were

significantly different from all other processes. and final consonant deletion and t/th

substitution were significantly different from final cluster reduction and f/th

141

 

Appendix A (Cont‘d)
substitution. Due to the fact that final r patterns appeared to differ from all of the other. it
was excluded from the final stimuli used in the dissertation study. Otherwise. data
corresponding to the processes of final consonant deletion differed from the process of
final cluster reduction. This line divided the processes into two categories: 1) more
salient (t/th substitution and final consonant deletion of t and k) and 2) less salient (f/th
substitution and Final Cluster Reduction of st and sk). This categorization will be used to
group the sounds to be tested into the two groups of more salient and less salient for

comparison.

142

Appendix B
Qualifying Questionnaire

I. How many years have you been practicing as an SLP?

2. How many clients have you had in the last two years who spoke African American
English?

a. <3
b. 3-5

c.>5

‘5

3. How frequently over the past three years have you communicated with someone who
speaks AAE?

a. < 5% of the time.
b. 5-15% of the time.
c. 15-25% ofthe time.

d. > 25% of the time.

4. Do you speak African American English? Yes No

Appendix C
Human Subjects Application
APPLICATION FOR INITIAL REVIEW
APPROVAL OF A PROJECT INVOLVING HUMAN SUBJECTS

Biomedical, Health Sciences Institutional Review Board (BIRB)
Social Science, Behavioral, Education Institutional Review Board (SIRB)
202 Olds Hall, Michigan State University
East Lansing, MI 48824-1047
Phone: (517) 355-2180
Fax: (517) 432-4503
E-mail: irb@msu.edu

Office Hours: M-F (8:00 A.M.-5:OO PM.)

Jacki—687316
‘ ID# 1022218 3

‘11_-_i.'.- Fu-"nLL'.-‘. .J

 

71a. " ; RespOnsible-PAr-OieCt Investigator: '
Name: Ida STOCKMAN
i ID#: xxx-xx-9247
l Department: Audiology

College: COM ARTS 8: SCI
Academic Rank: Professor '
Mailing Address: 371 Com. Arts and Sciences

1

Phone: 3-6764

'; Fax: 517-432-2370

Email: stockma1@msu.edu
.1b. gSeciOflndia'rylnvestigatorzi T

Name: Gregory Robinson

3 lD#: xxx-xx-5545

E Department: COMMUNICATION

§ College: COMM ARTS & $01

I

Academic Rank: NA
Mailing Address: 1028 Parker

144

Lansing, MI 48912

‘ Phone: 371-4345
Fax:
, Email: robin465@msu.edu
1c. Additional Investigators: T

I

I
: Peter R. LAPINE
i Dennis PRESTON

 

j Brad RAKERD
Q2". " anaemia.“ A ‘ “
lName:
ID#:
1 Department:
9 College:
: Academic Rank:
i Mailing Address:
2 Phone:
; Fax:
Email:
3‘. "I friendlier-eject: p'E'nEE‘p’ndné’dE KER—ICAN AMERICAN—ENv—GLISHHDIALECT DENSITY?
‘ BY SPEECH-LANGUAGE PATHOLOGISTS IN PREDOMINANTLY WHITE SCHOOL
DISTRICTS
:4. j Hfidveyodiévér’iecelvéd PreflliminaryApprovalfor—thisuprOjecit?’ * No
[5. iCategory OfflReviewM ”CT i i ' ' 72.751217 "
? i .EXPEDITED
86. ”TIE-{mg project being conducted to fulfrlltTrCrequrrements of an i ”PhD-h “ if
I education/training program? : Dissertation
7a. r Funding: i i i - Projec—tis not

145

7d _

' 8a.

8b.

€10.“
E11.
“12.
'13.

:14.
:15.

:16.

 

_.... _ _ _...___...h_ _.._

I The protection Of human subjects Often requires resources be dedicated fOr things
such as the consent process (space, personnel), the performance of the research

funded 7

(trained personnel interacting with subjects), care Of subject Issues or injuries
(counseling, medical care), confidentiality of data (space, equipment) and other
' monetary and non-monetary resources. Describe the resources that are available for
‘ this project for the protection Of human subjects.

1 The secondary investigator (Gregory Robinson) will be present during all data gathering tO
ensure that all rights are protected The data will be compiled and encoded through the
University scoring Office to maintain data integrity.

 

List all sites where this research will be conducted

Ionia ISD Office Oyer Speech- ”Language -Hearing Clinic

 

.-qu ..A .‘.4__...__-..-...-...

Do any Of these sites have their own IRB?

EDO you have any related project that were approved by an MSU IRB?

I (a) IRB Numbers: i021555

I Have you submitted this to anOther |RB(s)?

‘ ls anOther institution(s) relying on MSU' s IRB as the IRB of record?

__..,.__,_;__ -m.__- __._._V_._-_..~_

Are you using an FDA approved drug/devrceldragnostic test?

Are you using an FDA approved drug/device/diagnostic test for a

non FDA approved indication?

Has this protocol been submitted t6 the FDA or are there plans to

submit It to the FDA?

Does this prOject involve the use Of Materials of Human Origin (e. g.

1 human blood or tissue)?

.-_ ,-,_,_.._.....-......__s ...w—

Research Category

Education Research FF
Survey/Interview 17
AudiONideO Recording p-
Oral History I"
I Internet-based I—

' Analysis Of Existing Data {—

I International Research I—

Yes

Yes

Yes

Yes

Yes

Yes

Yes

v-" - ‘fimafi ,.--w.-.-.'. ...._--. ,2 -..E. “w —.-—...—._

Gene Transfer Research [-
Fetal Research
Medical Records
Stem Cell Research

Medical Imaging

71—1—17

Oncology

Clinical Trial (specify below)
I—

Surgical

'— Therapeutic

146

Yes

Yes

Yes

Yes

Yes

Yes

*YES

:NO
(NO

NO

.NO
NOW V

NO

a... ......._ ...mw- ..-——__._1...r_m_-__-. .. . ..-...- . ._ ...... -- ....... _--._..__.. . ‘-_.__._. _-__.-.._..-.__.

Prevention

Other

r_NO

' 17. Project Description (AbstraCt)

. i

I Dialect density” is used by language researchers tO describe inter-speaker differences within

5 dialect groups. When descriptions like these are used by lay persons, they often coincide with

E statements regarding comprehensibility (how easy tO understand the speaker was) or dialect I
Edetectability (how noticeable the dialect was). These judgments may affect speech- language
assessment and intervention. For example, a speech- language pathologist (SLP) may be

E more likely tO mistake a speaker of African American English (AAE) for a speech-language

‘ disorder in the public schools, particularly if the student is judged to be low in

E comprehensibility and high in dialect detectability. Since phonological features may be the

most noticeable, this study aims to answer the following question: How are judgments of

comprehensibility and dialect detectability influenced by the number and type of AAE

_ phonological features present within sentences? The hypothesis: sentences with more

E features and more salient features will impact judgments the greatest.

2 Certified SLPs from predominantly White school districts in mid-Michigan will listen to

. individual sentences containing varying amounts and types Of AAE phonological features.

I The SLPs will rate each sentence regarding perceived dialect detectability and

E comprehensibility. The ratings for the different sentences will be compared tO determine how

3 the number and type Of features present in each sentence contributed tO the SLPs’

- judgments

l

18. EProcedures

i The procedures will be organized according to the three different groups of subjects used in
i this study: 1) listeners, 2) speakers. and 3) naturalness raters.

i The lIsteners Will be gIven a hearing screening and answer a brief qualifyIng questionnaire.

E After qualifying for the experiment. they will view a Power Point presentation at a personal

; computer. The Power Point presentation will contain sound clips that contain sentences read

i with different types and occurrences Of phonological features commonly associated with

; AAE. The participants will listen tO the sound clips Over earphones tO block out ambient

' noise They will read instructions that orient them to the experiment tasks and the pragmatic ,
Icontext for the recorded sentences They will then listen to sound clips presented in individual

I sentences or in groups Of sentences. They will rating the clips regarding 1) how .
. understandable they believe the speakers would be tO the general population (1= Very easy

to understand, 5: Very difficult tO understand). and 2) how noticeable the use Of African _
American English is in each sentence (1= Not noticeable. 5= Extremely noticeable). They will

i record their responses onto a computer-readable “bubble" sheet with a nO. 2 pencil. After
completing all Of the ratings. each participant will place his/her response sheet into an
envelope and seal it.

E The speakers will be recorded using a headset microphone and digitized using Wavesurfer

I software (Sjolander & Beskow, 2004) at a sampling rate Of 22,050 Hz. Each speaker will be

‘ instructed to think about the context that will be presented to the listeners and naturalness
raters (See Verification Of Naturalness for a description Of the context) They will then be .
l encouraged to practice each sentence before recording. Once a self-determined comfortable '
I :intonation pattern is produced, the speakers will be instructed tO read each sentence again

. with the same intonation pattern. They will be recorded during this reading. but will not be

'E instructed tO use any specific AAE phonological features. These sentences will be used as

I phonetically consistent carrier phrases. Only the recordings Of the non-variable words will be

I used from this reading. Immediately following this first recorded reading, the speakers read

' the sentences aloud again. This time, they make specific phonological changes that either
correspond to an SAE production or one Of the conditions for AAE productions (varying in

147

A _ +— . .. -_...__...,_..— ..w— P..— -_-.
,_ A . -

number and type Of features included). The variable words from these recordings will be ,
i embedded into the appropriate position Of the previously recorded carrier phrases. I
I The naturalness raters will be given a hearing screening. If they pass the screening, they will

' then be presented with sound clips presented in a Power Point Presentation. Prior to the :
Iratings, the naturalness raters will view a series Of slides that orient them to their task and the
pragmatic context for the sentences they will be hearing. Each slide will include three .
different recordings that include exactly the same wording and planned phonological features. '
I The naturalness raters will listen to the three recordings on each slide tO determine which ‘
recording out of the three presented sounded the most natural, ” or the most like what would
actually be heard in conversation. They will be rating a total of 36 slides.

 

--~——‘:vw-' --M—d__.m____r__.-.,

19. I Does your investigathn involve incomplete disclosure Of the ' NO
I research purpose or deception Of the subjects? '

 

an." 1"". “ “-7

2%. Subject Population

I Listeners—16 White adult female Speech-Language Pathologists in predominantly White
‘ school districts.

Speakers—2 adult Female African Americans
I Naturalness Raters—6 adult African Americans (3 men and 3 women)

 

 

 

I20b. IAge range ofsubjects: VI18tO 65
I205. {IEQIJEI} populationmayinclude " i
I Target Populatlon: lncidentallnclusion:
I— Minors I— Minors
. I— PregnantWomen I; PregnantWomen
I“ Women Of Childbearing Age l’" Women Of Childbearing Age
I" Institutionalized Persons [— Institutionalized Persons
’ I— Students [7 Students
. In Low Income Persons [.7 Low Income Persons
E [‘7 Minorities '7 Minorities
; I— Prisoners l— Prisoners
l— HlV/AlDSlndividuals '— HlV/AlDSlndividuals 3
[— Psychiatric Patients '— Psychiatric Patients
i [— Incompetent Persons I— Incompetent Persons
I20d. ExpeCted number Of subjects (Including con—t‘rOl-sI—I I I E24-
I20em— Justify your sample—vs'i:#w MM“ .__..._.._..- w“ ”I W -

I

. Listeners= 16

E Speakers= 2

. Naturalness Raters= 6

' Power analysis for repeated measures ANOVA suggested at least a sample size of 9 for
Power- - ..80 Four CD compilations, require at least 4 subjects with each compilation.

"H“--.

 

 

._._. w-—_ __ _._..___ k.— -.._.4.___..__.__._.__ .,

I48

...fi...___.m. ”in. ,_._..._..__._ ._... ... ..._ .‘._.. ___..__._.._. ._._. _ ,,__,,_.. , -.A.” , ,_ .2... .2...... r, _, i,

20f. Describe the criteria for the inclusion of subjects.

E 1) Listeners—Certified SLP, White, working in predominantly White school district, minimal

' exposure to African American English, at least 2 years experience as SLP. 2) Speakers—

. Adult female, African American, Native Speakers of AAE, Able to include or not include AAE
J features in speech upon cue. 3) Naturalness Raters—African American adults, native

‘ speakers of AAE

 

__.._..._--.. ...__ -- ..h-....... _._ _ ._ __.. u.—__ -...-.-—. mar... . -....- _ _ ..-._- —...- . -.-_-.. _. ___.-...

20g. * Describe the criteria for the exclusion of subjects:

E 1) Listeners—not certified, not White, non-native English speaker, significant daily exposure

3 to AAE, less than 2 years SLP experience, failed hearing test. 2) Speakers—Not female, not ,
E African American, not native speakers of AAE, Unable to include or not include AAE features '
E upon cue. 3) Naturalness Raters—not African American, not native speakers of AAE, failed

1‘ hearing test.

 

___r ._-.—-_. m...“ _. .._........__._ v

20h(1) How will the subjects be recruited?

E
; The participants will be recruited from school districts in a rural mid-Michigan county that has

a student population that is 90% White/Caucasian-American and less than 5% African
EAmerican as indicated by the county‘s census from the preceding school year 2003 (Annie E. E
; Casey Foundation). The researcher will attend a county-wide SLP meeting. During the
E meeting the study will be described and a sign—up sheet will be distributed to collect contact .
3 information. One month prior to the experiment, the SLPs will be contacted via telephone and
' personal interaction for scheduling The SLPs will be compensated monetarily for their time.
The speakers will be recruited from a university theater program.
EEThe naturalness raters will be recruited through personal contacts

’2°h(2) EWiIl an advertisement be used? ‘ N O '

 

' zoi. » Are you associated with the subjects? gYES'

E (a) Nature of the association and what measures you are taking to
E protect subjects' rights, including safeguards against any coercion.

I work with many of the listeners as a fellow Speech- -Language
Pathologists i am not associated with the speakers or naturalness raters.

E 20j. EWill someone receive payment for recruiting ”the subjects? - NO

:20k._ Will the research subjects be compensated? YES N

L (1) Details concerning payment, including the amount and schedule
of payments including any conditions:

g Listeners: $15 (one time after completion of tasks or discontinuation)

E Speakers: $20 (one time after completion of tasks)

g Naturalness Raters: $10 (one time after completion of tasks or

E discontinuation)

 

 

 

 

20L” l Will the subjects incur additional financial costs as a result of their I NO

E E participation in this study? ‘
20m. ‘ Will this research be conducted with subjects in another country? f NO
20h. EWill this research be conducted with subjects in the U. S. from an E YES

ethnicE group of sub-group or other non-mainstream minorities

149

 

m ...fi..-_.——..—. _.._.“5 ...- .__.... ##7

Q (including non- English speakers)?
(1) What country/sub-group are they from?
African Americans

I (2) Does the different cultural context present any problems or risks
; that need to be addressed? If so, describe the issues and how you
will address them.

.No

 

 

21a. Risks and Benefits for subjects: Describe and assess any potential risks (physical,
' psychological, social, legal, economic) and assess the likelihood and seriousness of

3 such risks.

, Minimal risks associated with discomfort wearing headphones or viewing a computer screen

. 21b. I Describe procedures for protecting against or minimizing potential risks and provide
‘ . an assessment of their likely effectiveness.

I

Direct Supervision from secondary investigator Gregory Robinson.

 

 

 

21c. IAssess the potential benefits (if any) to be gained by the sujbects in this study, as well

as benefits which may accrue to society in general as a result of the planned work.

I
i

; Benefits to subjects? Payment, hearing screening

1 Benefits to Society? Understanding of perceptual obstacles that may impact the accuracy of I
. speech-language assessments with African American clients when conducted by White SLPs 1
I and may help inform goals and procedures of intervention with clients seeking dialect

I modification services from SLPs. The findings may also help inform theories of speech

I perception.

22a. 1 How will the subject' 3 privacy be protected? Include a description of who will be
3 interacting with the subjects or accessing and abstracting data from the subject's
records (academic, medical, etc.) and where the study will take place. For example, will ,
1 individuals not associated with the research study be present during the consent
I process and the conduct of the study?

1 No identifying information (except school district) responses pieced in sealed envelopes

.—~» ”4---... ____..__...._....... ._._.- m- —.~__._.... ..._..___..__....._ w_....__

_‘ 22b. f Where will the data be stored and for how long?

The paper responses will be stored in my personal office. Upon receipt, the data will be
entered into my person computer. The data will be stored on my personal computer located
in my personal office and will be backed up through my personal, password-protected e-mail
account as well as a compact disc The compact disc and the paper response sheets will be
kept indefinitely In my personal research archival files.

.-__- .....—.. q..- ~.—.—.-—.-o+.—.—..-___ .— -__.-.I-. .u—. ., -—-.

22c. IWho will have access to the research data?

[

Access to the data will be limited to me and my dissertation committee members: Ida
‘ Stockman, Peter Lapine, Dennis Preston, and Brad Rakerd.

22d. How will you ensure the confidentiality and/or anonymity of the research data? Include
, a description of the procedures and safeguards you will use, Including If identifying

[50

 

Iaiaim'auan will be stored-with the data.

‘ The security and integrity of the data will be ensured by backing up the files in three

‘ locations: on a personal web-based e-mail account. a compact disc. and a file on the hard ,
‘ drive of my personal computer The paper documents will be placed in my personal office in a 1
file cabinet The office will be locked when I am not present.’

 

..___ __ ___;-A_A_ _-___..,._.‘ s M... ____—__H. ._._,.__. .___ _.

 

, 23. Does this project involve protected health information as defined by NO
HIPAA?
I No
24. §(a) Select appropriate consent option. 7 Approval of a

consent form
and process .

4—‘—.

 

 

(b) Consent Procedures: l Gregory Robinson, will personally hand the
, consent form to each participant. The participants will sign a consent form
T (See appendix) that will state that there are minimal risks associated with
5 the experiment and will outline their rights. The participants will be asked
i to read the form carefully and ask me any questions about it that they
' might have. I will ask them if they have any questions about the form
before they begin the experiment.

i

 

 

 

 

25a. mi Have you or will you or a member of your immediate family receive, NO
‘ " from the sponsor of the research, financial or other forms of -

' compensation?
: 25b. Do or will you or a member of your immediate family have a ;' NO

significant financial interest in the company/agencylt” rm that is to
. sponsor the research?

 

25c i Are you submitting FDA form 3454 or 3455 (Conflict of Interest)? NO

 

 

26a. i When would you prefer to begin this project? ,4/15/2005
26b. iEstlmated duration of project: ; 04/15/2005

ADDITIONAL DOCUMENTS/ATTACHMENTS

01. 4/5/2005 Consent Form (i022218_ 4- 05- 05_ Consent Forms_ DIssertatIon pdf)

 

i 02. 4/5/2005 Other (i022218_4-05-05_METHOD_Human_Subjects.pdf)

 

03. 4/25/2005 Consent Form (i022218_4-22-05_Consent_Forms_Dissertation.pdf)

151

 

Appendix D
(.‘onsem F arms
.S‘peakers

Perceptions of African American English Dialect Density
by Anglo-European American Speech-Language Pathologists
Consent Form

You are being asked to participate in a study that examines the ability of speech-language
pathologists to comprehend and detect African American English (AAE) pronunciation patterns.

In this study. you will be asked to read a list of six sentences multiple times. During your
readings of the sentences. you will be asked to pronounce various words in the sentences in
different ways. You will be encouraged to practice reading the sentences aloud. Your readings
will be recorded through a headset microphone. You will be allowed to take a break in the
middle, if you like. The entire process should take approximately 1.5 to 2 hours to complete.

The risks associated with this experiment are minimal and involve primarily the discomfort with
wearing a headset. If you feel any discomfort in any way, please notify Greg immediately to
alleviate your discomfort.

Your participation in this study is completely voluntary. You will receive $20 for your
participation. If you choose to discontinue your participation at any point, for any reason. you
will still receive your payment.

Your privacy will be protected to the maximum extent allowable by law. Your identity will be
kept confidential to the other research participants. The following people will have access to the
data and recordings associated with the experiment: Gregory Robinson. Ida Stockman, Peter
Lapine, Brad Rakerd. and Dennis Preston.

If you have any questions about this study, please contact the investigator, Gregory C. Robinson, at
517-371-4345 or by e-mail at robin465@msu.edu. If you have questions or concems regarding your
rights as a study participant, or are dissatisfied at any time with any aspect of this study, you may
contact — anonymously, if you wish —Peter Vasilenko, Ph.D., Chair of the University Committee on
Research Involving Human Subjects (UCRIHS) by phone: (517) 355-2180. fax: (517) 432-4503. e-
mail: ucrihsfiimsuedu. or regular mail: 202 Olds Hall, East Lansing, MI 48824.

 

Your signature below indicates your voluntary agreement to participate in this study.

 

Printed Name Signature Date

 

Appendix D Continued
Consent Forms
Ar’atzn'uliless Raters

Perceptions of African American English Dialect Density
by Anglo-European American Speech-Language Pathologists
Consent Form

You are being asked to participate in a study that examines the ability of speech-language
pathologists to comprehend and detect African American English (AAE) pronunciation patterns.
Before beginning the experiment. you will be given a hearing screening which will show whether
you can hear tones presented over earphones. You will get the results of the screening
immediately after its completion. After passing the hearing screening, you may begin the
experiment.

In this study. you will view a Power Point presentation during which you will listen to sentences
through earphones. After listening to each group of sentences, you will be asked to indicate
which one out of the group was the most natural sounding. There will be a total of 72 groups of
sentences. You will be asked to take a 5 minute break in the middle. The entire experiment
should take approximately an hour to complete.

The risks associated with this experiment are minimal and involve primarily the discomfort with
wearing a headset and visual discomfort associated with looking at a computer screen during the
extent of the experiment. If you feel any discomfort in any way, please notify Greg immediately
to alleviate your discomfort.

Your participation in this study is completely voluntary. You will receive $10 for your
participation. After you pass the hearing screening, if you choose to discontinue your
participation at any point, for any reason, you will still receive your payment.

Your privacy will be protected to the maximum extent allowable by law. You will not be asked to
provide any identifying information on the response sheet except the school district in which you
work. After you complete your responses, you may place your response sheet into the envelope
provided so that your responses can not be associated with your identity. The following people
will have access to the data: Gregory Robinson (investigator), and his dissertation committee
members: Ida Stockman, Peter Lapine, Brad Rakerd, and Dennis Preston.

If you have any questions about this study, please contact the investigator, Gregory C. Robinson, at
517-371-4345 or by e-mail at robin465@msu.edu. If you have questions or concems regarding your
rights as a study participant, or are dissatisfied at any time with any aspect of this study, you may
contact — anonymously, if you wish —Peter Vasilenko, Ph.D., Chair of the University Committee on
Research Involving Human Subjects (UCRIHS) by phone: (517) 355-2180. fax: (517) 432—4503, e-
mail: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

Your signature below indicates your voluntary agreement to participate in this study.

 

Printed Name Signature Date

153

Appendix D C ()ntinued
Listeners

Perceptions of African American English Dialect Density
by Anglo-European American Speech-Language Pathologists
Consent Form

You are being asked to panicipate in a study that examines the ability of speech-language
pathologists to comprehend and detect African American English (AAE) pronunciation patterns.
Before beginning the experiment, you will be given a hearing screening during which will show
whether you are able to hear tones presented over earphones. You will get the results of the
screening immediately after its completion. After the hearing screening, you will be asked to
answer a brief questionnaire (4 questions) that will determine if you qualify for the study. After
passing the hearing screening and qualifyinO, you may begin the experiment.

In this study, you will view a Power Point presentation on a computer during which you will
listen to sentences through earphones. After listening to each sentence, you will be asked to make
two different perceptual ratings about the sentence. There will be a total of 42 sound clips. You
will be asked to take a 5 minute break between tasks. The entire experiment should take
approximately 30 minutes to complete.

The risks associated with this experiment are minimal. You may experience some discomfort
wearing the earphones and visual discomfort associated with looking at a computer screen during
the course of the experiment. If you feel any discomfort, please notify Greg immediately to
alleviate your discomfort.

Your participation in this study is completely voluntary. You will receive $15 for your
participation. After qualifying for the study, if you choose to discontinue your participation at
any point. for any reason, you will still receive your payment.

Your privacy will be protected to the maximum extent allowable by law. You will not be asked to
provide any identifying information on the response sheet except the school district in which you
work. After you complete your responses, you may place your response sheet into the envelope
provided so that your responses can not be associated with your identity. The following people
will have access to the data: Gregory Robinson, the investigator, and his dissertation committee
members: Ida Stockman, Peter Lapine, Brad Rakerd, and Dennis Preston.

If you have any questions about this study. please contact the investigator, Gregory C. Robinson. at
517-371-4345 or by e-mail at robin465@msu.edu. If you have questions or concems regarding your
rights as a study participant, or are dissatisfied at any time with any aspect of this study, you may
contact — anonymously, if you wish —Peter Vasilenko, Ph.D., Chair of the University Committee on
Research Involving Human Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517)432-4503. e-
mail: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

 

Your signature below indicates your voluntary agreement to participate in this study.

 

Printed Name Signature Date

 

Appendix E
Naturalness Rating Response Form
Which sentence sounded the most like you might hear someone say it in real life?

Write a letter a. b, or c corresponding to the sentence that you felt sounded the most
natural.

I 19.
2 20.
3 21.
4 22
5 23
6 24.
7 25.
8 26.
9 27.
10. 28.
11. 29.
12 30
13. 31.
14. 32.
15. __ 33.
16. 34.
17. 35.
18. 36.

155

Naturalness Rating Results

Appendix F

The naturalness raters selected one sentence out of each set of three recordings (A, B. or
C) that sounded the most natural to them. The table displays the “result,” or the
recording that was chosen most frequently by the six raters for each stimulus sentence.
Phonological Features: F Dk=Final Deletion of /k/, F Dt=F inal Deletion of /t/, FRk=Final
Cluster Reduction of sk, FRt=Final Cluster Reduction of st, t/th=Substitution of [t] for
/0/, and f/th=Substitution of [f] for /0/. NR = Naturalness Rater.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

f/th

 

f/th

 

 

 

 

 

f/th

 

 

 

 

 

 

 

Stimulus Sentence Compilation 1 Compilation 2 Results
Type of No. of
Speak- Fea- Fea- NR NR NR NR NR NR Re- To-
# er tures tures 1 2 3 1 2 3 suit tai
1 1 FDk 0 A A A C C A A 4
2 1 FDk 1 A C C C B B C 3
3 1 FDk 3 A B B C B A B 3
4 1 F Dt 0 C C C C B B C 4
5 1 FDt 1 B C C B C C C 4
6 1 FDt 3 B C C C C C C 5
7 1 t/th 0 A B A A A B A 4
8 1 t/th 1 B A B B B C B 4
9 1 t/th 3 B B B B B B B 6
10 1 FRk 0 A C C B C C C 4
11 1 F Rk 1 C B C C C A C 4
12 1 FRk 3 A B B B C C B 3
13 1 F Rt 0 C B C C B C C 4
14 1 FRt 1 A B C C C B C 3
15 1 FRt 3 A A C C C B C 3
16 1 f/th O A C B A B A A 3
17 1 f/th 1 C C A A A A A 4
18 1 f/th 3 B B C A C C C 3
19 2 F OK 0 A B C C C C C 4
20 2 FDk 1 A A C C B C C 3
21 2 FDk 3 B B A B B A B 4
22 2 FDt O A A C C C C C 4
23 2 F Dt 1 C C C C B B C 4
24 2 FDt 3 C A C A A A A 4
25 2 t/th 0 B B B B B B B 6
26 2 t/th 1 B B C C C A C 3
27 2 t/th 3 A B B A B B B 4
28 2 F Rk O C B C C B C C 4
__29 2 F Rk 1 B B C C C C C 4
3O 2 FRk 3 C B C C B C C 4
31 2 FRt O A B A B B A B 3
32 2 FRt 1 A C A C C C C 4
33 2 FRt 3 C C B B C C C 4
34 2 O A C A C C C C 4
35 2 1 C C B C B C C 4
36 2 3 C B C B B B B 4

 

 

156

 

REFERENCES

Adams, C. L. ( I981). The influence of counselor race, attire, and speech style on Black
juvenile delinquents' perception of counselor attractiveness and ability to be
warm, empathic. and genuine. Dissertation Abstracts International, 42(3-A).
1324.

Alvarez, L., & Kolker, A. (Writer). & L. Alvarez & A. Kolker (Director) (1986).
American Tongues [Motion Picture]. In L. Alvarez & A. Kolker (Producer).
United States: Center for New American Media, Inc.

Anderson. R. T. (2002). Practical assessment strategies with Hispanic students. In A. E.
Brice (Ed.), The Hispanic child: Speech, language, culture and education. (pp.
143-184). Boston, MA: Allyn and Bacon Press.

Annie E. Casey Foundation. (2003). KIDS Count Data. Baltimore. MD: Author.

ASHA. (1983a). Implications ofthe position on social dialects. Asha, 25(9). 25-27.

ASHA. (1983b). Social dialects. Asha. 25(9). 23-24.

ASHA. (1995). We can do better. Recruiting, retaining. and graduating African American
students. Asha, 3 7(2), 54-56.

ASHA. (1999, May 2004). Standard 3.0 Curriculum (Academic and Clinical Education).
Retrieved December 17, 2004, 2004, from
http://www.asha.orgr’abOIIL/academia’accreditation/standards.htm

ASHA. (2001). Scope ofpractice in speech-lung:rage pathology. Rockville. MD: Author.
ASHA. (2005. February 8, 2006). Highlights and trends: ASHA member counts for 2005.

Retrieved February 25. 2006 from
http://\\\nv.asha.org/memberslrescarch/rcports/member-counts.htm

 

Bailey. G.. & Thomas. E. R. (1998). Some aspects of African American Vernacular
English phonology. In S. Mufwene. J. Rickford, G. Bailey & J. Baugh (Eds).
African American English (pp. 85-109). London: Routledge.

Battle. D. E. (2002.). Communication disorders in multicultural populations (3rd ed.).
Boston, MA: Butterworth—Heinemann.

Baugh. J. (1983). Black street speech. Austin. TX: University of Texas Press.

Belton, M. A. (1984). The effect of therapist dialect upon the Black English speaking
patient's perception of therapist empathy, warmth, genuineness, and expertlse.
Dissertation Abstracts International, 44(8-B), 2547.

Brennan. E., & Brennan. J. (1981a). Accent scaling and language attitudes: Reaction to
Mexican-American English speech. Language and Speech, 24, 207-221.

Brennan, E., & Brennan, J. (1981b). Measurements of accent and attitude toward
Mexican American speech. Journal of Psycholinguistic Research. 10, 487-501.

Brice. A. E. (2002). Normal Processes of second language acquisition. In A. E. Brice ,
(Ed). The Hispanic child: Speech, language, culture and education. (pp. 37-53).
Boston, MA: Allyn and Bacon Press.

Brook, G. L. (51970). The ltmguage of Dickens. London: Andre Deutsch.

Burda. A. N., Scherz, J. A., Hageman, C. F., & Edwards, H. T. (2003). Age and
Understanding Speakers with Spanish or Taiwanese Accents. Perceptual and
Motor Skills, 97(1), 11-20.

C heng. L. L. (1987). Assessment procedures. In L. L. Cheng (Ed.), Assessing Asian
language performance (pp. 135-159). Rockville, MD: Aspen Pubhshers.

Cohen. J. (1988). Statistical power analysisfor the behavioral sciences (2nd ed.).
Hillsdale, NJ: Lawrence Earlbaum Associates.

Craig. H. K., Thompson, C. A., Washington. J. A., & Potter, S. L. (2003). Phonological
features of child African American English. Journal of Speech-Langm‘tge-
Hearing Research, 46(3), 623-635.

Craig, H. K., & Washington, J. A. (2000). An assessment battery for identifying language
impairment in African American children. Journal of Speech-Language-Hearmg
Research, 43(2), 366-379.

Craig, H. K.. & Washington, J. A. (2002). Oral language expectations for African
American preschoolers and kindergartners. American Journal of Speech-
Language-Pathway, l 1 (1), 59-70.

Craig, H. K., & Washington, J. A. (2004). Grade-related changes in the production of
African American English. Journal of Speech-Language-Hearing Research.
47(2), 450-463.

Craig. H. K., Washington, J. A., & Thompson-Porter, C. (1998a). Average C-unit lengths
in the discourse of African American children from low-income, urban homes.
Journal QfSpeech-Language-Hearing Research, 41(2). 433-444.

Craig. H. K., Washington, J. A.. & Thompson-Porter. C. (1998b). Performances of young
African American children on two comprehension tasks. Journal of'Speech-
L(mgrurge-Hearing Research, 41(2), 445—45 7.

Dale. C .V. & Gurevitz. M. (1995. October 17) Legal analvsis of proposals to make
English the official language ofthe United States. Washington, DC:
Congressional Research Service.

Dallal. G. E. (2003. July 27, 2003). Randomization. com. Retrieved January 12, 2005.
from http://wuw.randomization.com

 

de Bardelaben. G. (1980). Examiner's democratic values, Black dialect and the clinical
perception of intelligence. Dissertation Abstracts International. 41(1-B), 346-347.

Derwing. T. M., & Munro, M. J. (1997). Accent, intelligibility and comprehensibility:
Evidence from four Lls. Studies in second language acquisition, 19(1), 1-16.

Edwards. J. R. (1982). Language attitudes and their implications among English speakers.
In E. B. Ryan & H. Giles (Eds), Attitudes toward language variation (pp. 20-33).
London: Arnold.

Fasold. R. W. (1981). The relation between Black and White speech in the South.
American Speech, 56(3), 163-189.

Fasold. R. W. (1999, May). Ebonic need not be English. Paper presented at the
Georgetown University Round Table, Washington DC.

Flege. J. E.. & Fletcher. K. L. (1992). Talker and listener effects on degree of perceived
foreign accent. Journal ofthe Acoustic Society of America, 91 (1), 370-389.

F lege. J. E., & Hammond, R. H. (1982). Mimicry of non-distinctive phonetic features
between language varieties. Studies in Second Language A cquisition.. 5, 1-17.

Gass. S.. & Varonis. M. (1984). The effect of familiarity on the comprehensibility ofnon-
native speech. Language Learning, 34, 65-90.

Gerken. K. C.. & Deichmann. J. W. (1979). The ability of listeners to report oral
responses of Black and White children. Language. Speech, and Hearing Services
in Schools, 10(1), 35-46.

Googlc. (2005). Google search engine. Retrieved February 15. 2005. 2005. from
http://\mvw.google.com

159

Greenberg, S., Hollenback, J. and Ellis. D. (1996) Insights into spoken language gleaned
from phonetic transcription of the Switchboard corpus. In Proceedings ofthe
Fourth International Conference on Spoken Language (ICSLP), Philadelphia.
824-27

Graff, D., Labov, W., & Harris, W. (1983). Testing listeners' reactions to phonological
markers of ethnic identity: A new method for sociolinguistic research. In D.
Sankoff (Ed), Diversity and diachrony (pp. 45-58). Amsterdam/Philadelphia:
Benjamins.

Gutierrez-Clellen. V. F.. C alderon, J .. & Weismer, S. E. (2004). Verbal working memory
in bilingual children. Journal (y‘Speech. Lang-zu‘tge, and Hearing Research. 4 7(4),
863-876.

Guy. G. (1980) Variation in the group and the individual: The case of final stop deletion.
In W. Labov (Ed), Locating language in time and space (pp. 1-36). New York:
Academic Press.

Hayes, J .R., & Hatch, J .A. (1999) Issues in measuring reliability: Correlation versus
percentage of agreement. Written Communication, 16(3), 354-367.

Hemingway, B. L., Montague, J. C ., & Bradley, R. H. (1981). Preliminary data on
revision of a sentence repetition test for language screening with Black first-grade
children. Language, Speech. and Hearing Services in the Schools. 12, 145-152.

Hodson, B.W. (2004) Hodson Assessment o/‘Phonological Processes (3rd edition: HAPP-
3). Austin. TX: Pro-Ed.

Irvine, J. T. (2001 ). “Style” as distinctiveness: The culture and ideology of linguistic
differentiation. In J .R. Rickford & P. Eckert (Eds) Style and sociolinguistic variation
(pp. 21-43). New York: Cambridge University Press.

J ohansson, S. (1978). Studies of error gravity--Native reactions to errors produced by
Swedish learners of English (Gothenburg Studies in English No 44). Gothenburg.
Sweden: Department of English, University of Gothenburg.

Johnson, F. L., & Buttny, R. (1982). White listeners' responses to "sounding black" and
"sounding white": The effects of message content on judgments about language.
Communication Monographs, 49(1), 33-49.

Johnston. J. R., & Wong, A. M.-Y. (2002). Cultural differences in beliefs and practices

concerning talk to children. Journal of Speech, Language, and Hearing Research,
45(5). 916-926.

160

Junker. D. A.. & Stockman. I. J. (2002). Expressive vocabulary of German-English
bilingual toddlers. American Journal ofSpeech Language Pathologv, 1 l, 381-
394.

Kent. R. D., Miolo. G., & Bloedel. S. (1994‘). The intelligibility ofchildren's speech: A
review of evaluation procedures. American Journal ofSpeech Language
Pathologv. 3, 81—95.

Klee. T.. Stokes. S. F ., Wong. A. M.-Y., F letcher, P., & Gavin. W. J. (2004). Utterance
length and lexical diversity in Cantonese-speaking children with and without
specific language impairment. Journal of Speech. Language, and Hearing
Research. 47(6), 1396-1410.

Kuhl. P. K. ( 1994). Learning and representation in speech and language. Current Opinion
in Neurobiologv, 4(6), 812-822.

Labov, W. (1966). The social stratification ofEnglish in A-"ew York C ity. Philadelphia:
The Center for Applied Linguistics.

Labov. W. & Ash. S. (1997). Understanding Birmingham. In Bernstein, Nunnally &
Sabino (Eds). Language variety in the south revisited (pp. 508-5 73). Tuscaloosa:
University of Alabama

Lambert, W. E., Hodgson. R., Gardner. R. C.. & Fillenbaum, S. (1960). Evaluational
reactions to spoken languages. Journal ofAbnormal and Social Psychologi-r. 60.
44-51.

Lane. H. (1963). Foreign accent and speech distortion. Journal ofthe Acoustical Society
ofAmerica, 35, 451-453.

Leonard. L. B. (1989). Language leamability and specific language impairment in
children. Applied Psycholinguistics, 10. 179-202.

Leonard. L. B. (1999,). The study of language acquisition across languages. In 0. L.
Taylor & L. B. Leonard (Eds), Language acquisition across North America:
C ross-cultural and cross-ling:tistic perspectives (pp. 21-38). San Diego: Singular
Publishing Group.

Levey. S. (2004). Speech-language pathology students' awareness of linguistic and
cultural differences. Perspectives on issues in higher education (American

Speech-Language-Hearing Association Division 10), 7(2). 2-5.

Linguistic Society of America. (1997). Resolution of the Linguistic Society of America
on the Oakland "Ebonics" issue. The Modern Language Journal, 81, 14.

Luelsdorff. P. (1975). A segmental phonology ofB/ack English. The Hague: Mouton.

161

Malmkjaer, K. (Ed). (1991). The linguistic encyclopedia. New York and London:
Routledge.

Marston, A. D. (1974). The effect of American regional dialects upon speaker credibility
and perceived personality. Dissertation Abstracts Internationc'tl, 34 (9-A, Pt 2),
6154-6155.

Massaro, W. (.1998) Perceiving talkingfaces (from speech perception to a behavioral
principle). Cambridge: The MIT Press.

McCarthy, J. (1986). OCP Effects: Gemination and antigemination. Language Inquiry.
17, 207-263.

Mulac. A.. & Rudd, M. J. (1977). Effects of selected American regional dialects upon
regional audience members. Communication Monographs, 44(3), 185-195.

Mufwene, S. (1998) What research on creole genesis can contribute to historical
linguistics. In M. Schmid, J. Austin, and D. Stein (Eds) Historical linguistics 1997
(pp. 315-338). Amsterdam: John Benjamins.

Munro. M. J ., & Derwing, T. M. (1995a). Foreign accent, comprehensibility, and
intelligibility in the speech of second language learners. Language Learning, 49
(Supplement 1), 285-310.

Munro. M- J ., & Derwing. T. M. (1995b). Processing time, accent, and comprehensibility
in the perception of native and foreign-accented speech. Language and Speech. 3 8
(Part 3), 289-306.

Munro, M. J., Derwing. T. M., & F lege. J. E. (1999). Canadians in Alabama: A
perceptual study of dialect acquisition in adults. Journal of Phonetics, 27(4). 385-
403.

Nabeleck, A.. & Donahue, A. (1984). Perception of consonants in reverberation by native
and non-native listenders. Journal of the Acoustical Society of America, 75 . 632-
634.

Nelson, N. W., & McRoskey, R. L. (1978). Comprehension of Standard English at varied
speaking rates by children whose major dialect is Black English. Journal of
Communication Disorders, 11(1), 37-50.

Nicolosi, L.. Harryman, E.. & Kresheck, J. (2003). T erminologv in communication
disorders (5th ed.). Baltimore, MD: Lippincott Williams & Wilkins.

Niedzielski, N. (1999). The effect of social information on the perception of

sociolinguistic variables. Journal ofLanguage and Social Psychology, 18(1). 62-
85.

162

Niedzielski, N.. & Preston. D. (2003). Folk Linguistics. Berlin: Mouton de Gruyter.

Oetting. J. B.. & McDonald. J. L. (2001). Nonmainstream dialect use and specific
language impairment. Journal of Speech. Language, Hearing Research. 44(1).
207-223.

Oetting, J. B., & McDonald. J. L. (2002). Methods for characterizing participants'
nomnainstream dialect use in child language research. Journal ofSpeech-
Lt‘tnguage-Hearing Research, 45(3). 505-518.

Payne. K.T., & Taylor. CL. (2005). Multicultural differences in human communication
disorders. In N. B. Anderson & G.H. Shames (Eds). Human con-mumication
disorders (7"1 edition). Boston: Pearson.

Pena. E.. Iglesias. A.. & Licz, C. (2001). Reducing test bias through dynamic assessment
of children's word learning ability. American Journal of Speech-Language
Pathologv, 10, 138-154.

Pierce. C .A.. Block. R.A. & Aguinas. H. (2004) Cautionary note on reporting eta-squared
values from multifactor ANOVA designs. Educational and Psychological
Measurement, 64(6), 916-924.

Pisoni. D. B., & Lively, S. E. (1995). Variability and invariance in speech perception: A
new look at some old problems in perceptual learning. In W. Strange (Ed.),
Speech perception and linguistic experience: Issues in cross-language research
(pp. 433-459). Baltimore: York Press.

Plichta. B.. & Rakerd. B. (2002. October). Perceptions oft/a/jronting in two Michigan
dialects. Paper presented at the NWAV Conference, Stanford, CA.

Pollock. K. E., Bailey. G., Berni. M. C., Fletcher, J. M., Hinton, L.N., Johnson. 1.. &
Weaver, R. (1998, March 17, 2001). Phonological features of African American
Vernacular English (AA VE). Retrieved October 24, 2004, from
http:x’t’wvnv.rehabmed.ualbena.ca/spa/phonology/features.htm

Preston. D. (1996a). W haddayaknow: The modes of folk linguistic awareness. Language
A ut'areness. 5 ( l ). 40-74.

Preston. D. ( 1996b). Where the worst English is spoken. In E. Schneider (Ed), Focus on
the USA (pp. 297-360). Amsterdam: John Benjamins.

Preston, D. (1997). The South: The touchstone. In Bernstein. Nunnally & Sabino (Eds).

Language variety in the south revisited (pp. 31 1-351). Tuscaloosa: University of
Alabama.

163

Preston, D. (1999). A language attitude approach to the perception of regional variety. In
D. R. Preston (ed.). Handbook ofperceptual dialectology (pp. 359-73.).
Amsterdam: John Benjamins,

Preston, D., & Howe, G. M. (1987). Computerized generalizations of mental dialect
maps. In Keith Denning (Ed.), Variation in language: NWA V-XV at Stanford (pp.
361-378). Stanford: Department of Linguistics, Stanford University.

Preston. D., & Robinson. G. (2005). Dialect Perception and Attitudes to Variation. In M.
J. Ball (Ed), Clinical Sociolinguistics (pp. 133-150). Oxford: Blackwell.

Prince, A. & Smolensky. P. (1993): Optimality Theory: Constraint Interaction in
Generative Grt‘tmmar. Rutgers University Center for Cognitive Science Technical
Report 2.

Pumell, T., Idsardi. W., & Baugh. J. (1999). Perceptual and phonetic experiments on
American English dialect identification. Journal of Language and Social
Psychology, 18.1 (Special issue: Attitudes, Perception, and Linguistic Features).

Qi. C. H., Kaiser, A. P., Milan, S. E.. Yzquierdo, Z., & Hancock, T. B. (2003). The
perfomiance of low-income, African American children on the Preschool

Language Scale-3. Journal of Speech-Language and Hearing Research, 46(3 ).
576-590.

Rickford, J .R., & Rickford, R.J. (2000) Spoken soul: The story ofB/ack English. New
York: John Wiley & Sons.

Robinson. P. (1973). Testing the second-language competence of children and adults.
English Language Teaching. 27, 190-199.

Ryan, E., Carranza, M., & Moffte, R. (1977). Reactions toward varying degrees of
accentedness in the speech of Spanish-English bilinguals. Language and Speech.
20, 267-273.

Schiavetti, N. & Metz, DE. (1997) Evaluating research in communicative disorders (5m
edition) Boston: Pearson.

Schmid, P. M., & Yeni-Komshian, G. H. (1999). The effects of speaker accent and target
predictability on perception of mispronunciations. Journal of Speech-Lang and
Hearing Research, 42(1), 56-64.

Schraeder, T., Quinn, M., Stockman, I.J., & Miller, J. (1999) Authentic assessment as an

approach to preschool speech-language screening. American Journal ofSpeech-
Language Pathology), 6, 195-200.

164

Skutnabb-Kangas, T. (2000). Linguistic genocide in education-or worldwide diversity
and human rights? Mahwah. NJ: Lawrence Erlbaum Associates.

Seymour. H. N., & Bland, L. (1991). A minority perspective in the diagnosis of child
language disorders. Clinical Communication Disorders, 1(1). 39-50.

Seymour, H. N., & Pearson, B. Z. (2004). Steps in designing and implementing an
innovative assessment instrument. Seminars in Speech and Language, 25 ( 1 ), 27-
31.

Seymour, H. N., & Ralabate, P. K. (1985). The acquisition of a phonologic feature of
Black English. Journal ofCommunication Disorders, 18(2), 139-148.

Seymour, H. N., Roeper, T.. & deVilliers, J. G. (2003). Diagnostic evaluation of
language variation (DEL V) criterion-referenced San Antonio, TX: The
Psychological Corporation.

Seymour. H. N.. & Seymour, C. M. (1981). Black English and standard American
English contrasts in consonantal development of four and five-year old children.
Journal ofSpeech and Hearing Disorders, 46(3). 274-280.

Sjolander. K., & Beskow. J. (2004). W avesurfer (Version 1.7.1). Stockholm, Sweden:
Centre for Speech Technology.

Smith. E. A. (1993). Cultural and linguistic factors in worker notification to blue collar
and no-collar African-Americans. American Journal of Industrial Medicine,
23(1), 37-42.

Smitherman. G. (1977). T alkin' and testitfvin': The language of Black America. Boston:
Houghton-Mifflin.

Smitherman. G. (1994*). Black talk: Words and phrases/rom the hood to the amen
corner. Boston: Houghton-Mifflin.

Stein. S. J. (1981). C Iient's perception of counselor trustworthiness, expertness. and
attractiveness as a function of counselor race and dialect. Dissertation Abstracts
Internatiomzl, 42(6-B), 2552-25 53.

Steriade, D. (2004). The Phonology ofPerceptibility Effects: the P-map and its
consequences for constraint organization. Retrieved 11-20-04, 2004, from
wuvulinguisticsucla.edu/people/ steriade/papers/P-map_for_phonology.doc

Stockman, I. J. (in press) Alveolar bias in the final consonant deletion patterns of African
American children. Language, Speech. and Hearing Services in the Schools.

 

Stockman, I. J. (1986). Language acquisition in culturally diverse populations: the Black
child as a case study. In O. L. Taylor (Ed), Nature of communication disorders in
culturally and linguistically diverse populations (pp. 1 17-155). San Diego. CA:
College-Hill Press.

Stockman, I. J. (1996). Phonological development and disorders in African American
children. In A. G. Kamhi, K. E. Pollock & J. L. Harris (Eds), Communication
development and disorders in African American children (pp. 1 17-153).
Baltimore: Paul H. Brooks.

Stockman, I. J. (1997a). The promises and pitfalls of language sample analysis as an
assessment tool for linguistic minority children. Language . Speech. and Hearing
Services in the Schools, 27, 355-366.

Stockman, I. J. (1997b). The sociopolitical construction of science: Evidence from the
language research of African American children. Archives ofthe W. E. B. Du Bois
Distinguished Visiting Lecturer Series. New York. NY: The Graduate School and
University Center of The City University of New York.

Stockman. I. J. (2000). The new Peabody Picture Vocabulary Test - III: An illusion of
unbiased assessment. Language , Speech. and Hearing Services in the Schools.
31. 340-353.

Stockman, I. J. (2004). Clinical phonology issues in serving speakers of African-
American Vernacular English. In R. D. Kent (Ed). The MIT Encyclopedia of
Communication Disorders (pp. 158-160). Cambridge, MA: The MIT Press.

Stockman. I. J.. Boult. J. R., & Robinson. G. (2004). Multicultural issues in academic and
clinical education: A cultural mosaic. ASHA Leader (20), 6-7.

Stockman, I.J., Guillory. B., Newkirk, B., & Seibert, M. (in submission) Regional
variation among African American preschoolers in standardized language test
scores and dialect density.

Stockman, I. J ., & Schraeder, T. (1999). The concept ofa minimal competence core
shows assessment promise. Asha, 41(4), 50-51.

Stockman, I. J ., & Vaughn-Cooke, F. B. (1991). Child language acquisition research: A
neglected area of focus in the Black African diaspora. In M. Cummings, L. Niles
& 0. Taylor (Eds), Handbook on communications and development in Africa and
the African diaspora (4th ed, pp. 82-129). Needham, MA: Ginn Press.

Taylor. 0. L. (1999). Cultural issues and language acquisition. In 0. L. Taylor & L. B.

Leonard (Eds). Language acquisition across North America: Cross-Cultural and
C toss-linguistic Perspectives (pp. 21-38). San Diego: Singular Publishing Group.

166

 

Terrell. S. L., & Jackson, R. S. (2002). African Americans in the Americas. In D. E.
Battle (Ed). C ommunicc‘ttion disorders in multicultuml populations (3rd ed, pp.
33-70.). Boston, MA: Butterworth-Heinemann.

Thomas. J.E. (1998) Properties of the ANOVA F test as applied to ordered categorical
data : a simulation-based study. Unpublished doctoral dissertation, Baylor
University.

US. Department of Special Education Programs. (2004). IDEAdata. org. Retrieved
October 10, 2004, 2004, from http://vmvv.ideadata.org/index.html

Van Riper. C. (1978’). Speech correction. Englewood C liffs, NJ: Prentice Hall.

Voi gt. D. A. (1994). The effects of the dialect of speaker and qualifications of the speaker
on rater's perceptions of a job applicant. Dissertation Abstracts International:
Section B: The Sciences and Engineering. 5 4(7-B). 3886.

Washington. J. A.. & Craig. H. K. (1992). Performances of low-income. African
American preschool and kindergarten children on the Peabody Picture
Vocabulary Tests-Revised. Language , Speech. and Hearing Services in the
Sclmols. 23. 329-333.

Washington, I. A.. & Craig, H. K. (1994.). Dialectal forms during discourse of poor.
urban. African American preschoolers. Journal of Speech and Hearing Research.
37(4). 816-823.

Washington. I. A.. & Craig, H. K. (1998). Socioeconomic status and gender influences
on children's dialectal variations. Journal of Speech-Language and Hearing
Research. 41(3). 618-626.

Washington. I. A.. C raig, H. K., & Kushmaul, A. J. (.1998). Variable use of African
American English across two language sampling context. Journal ofSpeech-
Language and Hearing Research, 41(5). 1 1 15 -I 124.

Westby. C. (1990). There's no such thing a culture-free testing. Texas Journal of
Audiologv and Speech Pathology. 16(1). 4-5.

Wilson. F. W ., Wilson, J. R., & C oleman, T. J. (2000). Culturally appropriate assessment:
issues and strategies. In T. J. Coleman (Ed), Clinical management of
communication disorders in children (pp. 101-127). Needham Heights. MA:
Allyn and Bacon.

Wolfram, W. (1986). Language variation in the United States. In Orlando L. Taylor (ed).

Communication problems in culturally and linguistically diverse populations.
College-Hill Press. 73-115.

167

Wolfram. W., & Schilling-Estes. N. (.1998). American English. Malden. MA: Blackwell
Publishers.

Wong. A. M.-Y., Leonard. L. B., Fletcher. P., & Stokes. S. F. (2004). Questions without
movement: A study ofCantonese-speaking children with and without specific

language impairment. Journal of Speech. Language. and Hearing Research.
47(6), 1440-1453.

Wyatt. T. A. (2002). Assessing the communicative abilities ofclients from diverse
cultural and language backgrounds. In D. E. Battle (Ed), Communication
disorders in multicultural populations. (3rd ed. pp. 415-459). Boston, MA:
Butterworth-Heineman.

168