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INTERDISCIPLINARY PERSPECTIVES ON THE
NORHTERN CITIES CHAIN SHIFT

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Barthomiej Plichta

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INTERDISCIPLINARY PERSPECTIVES ON THE NORHTERN CITIES CHAIN
SHIFT

By

Bartlomiej Plichta

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Linguistics and Germanic, Slavic, Asian and African Languages

2004

ABSTRACT

INTERDISCIPLINARY PERSPECTIVES ON THE NORHTERN CITIES CHAIN
SHIFT

By

Bartlomiej Plichta

This dissertation concerns sociophonetic aspects of the Northern Cities Chain Shift
(N CCS). An interdisciplinary research paradigm is proposed whereby certain
articulatory-phonetic and perceptual processes are integrated into a heater-mediated
model of sound change. Two studies are presented — one is speech production, and one in
speech perception. The production study demonstrates that vowel nasalization is
distributed along the NCCS isoglosses of sex and region. The perception study shows that
sociophonetic information is utilized in speech perception. The two studies come together
in an argument that the NCCS vowel space is being reshaped by micro-level articulatory-

perceptual processes negotiated by the speaker, hearer, and the speech community.

ACKNOWLEDGEMENTS

I am grateful to Professors Dennis Preston and Brad Rakerd who have inspired and

supervised this work with their tremendous expertise and professionalism.

I would like to thank Professor Dennis Preston and Professor David Prestel for their

support in the development of the Macintosh version of A kustyk.

This work has been made possible in part by the National Science Foundation Award

#353162.

Finally, I would like to express my gratitude to open-source and scholarly communities
whose work has made this project possible (in alphabetical order): Electronic
Metastructure for Endangered Languages Data (EMELD.org), Linguistlistcrg,

OpenOffice.org, Praat, Scalable Vector Graphics (SVG), Commozm, Tcl/Tk.

iii

PREFACE

This dissertation has been formatted in
compliance with the Michigan State
University Graduate School dissertation
formatting guidelines.

Statistics reports and citations have been
formatted in compliance with the 5'” Edition
APA Publication Manual.

Figures on pages: xiii, 7, I4, 25, 27,28,33
41, 42, 47, 47, 48, 49, 50, 52, 54, 63, 84, 86,
87, 91, 97, 97, 100, 122 were generated by
Akustyk (Plichta, 2004) and the EPS
rendering library by Boersma & Weenink
(2002).

iv

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ............................................................................................... iii
PREFACE .......................................................................................................................... iv
TABLE OF CONTENTS ..................................................................................................... v
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
KEY TO SYMBOLS AND ABBREVIATIONS ............................................................ xiii
CHAPTER 1 INTRODUCTION ................................................................................... 1
1.1 THE GOALS OF THIS DISSERTATION ........................................................................... 1
1.2 VARIATIONIST SOCIOPHONETICS ............................................................................... 2
1.2.1 This dissertation as a work in variationist sociophonetics .......................... 3
1.2.2 Current state of the field of sociophonetics ................................................ 3
1.3 SOCIOPHONETIC STUDIES OF SPEECH PRODUCTION ................................................... 4
1.3.1 Quantitative studies of consonantal variation ............................................. 4
1.3.2 Quantitative studies of vocalic variation ..................................................... 6
1.3.3 Sociophonetic accounts of suprasegmental features ................................... 8
1.4 SOCIOPHONETIC STUDIES OF SPEECH PERCEPTION ..................................................... 9
1.4.1 Perceptual dialectology ............................................................................... 9
1.4.2 Experimental studies of speech perception ............................................... 10
1.4.3 Needed research in perceptual sociophonetics ......................................... 16
1.4.4 Speech Science —similar questions, different methodologies ................... 17
1.5 ORGANIZATION ....................................................................................................... 21
1.5.1 The speech production study .................................................................... 21
1.5.2 The speech perception study ..................................................................... 21
1.5.3 Comments and conclusions ....................................................................... 22
CHAPTER 2 VOWEL NASALIZATION AND NCCS ............................................. 23
2.1 INTRODUCTION ....................................................................................................... 23
2.2 FRONTING AND RAISING .......................................................................................... 24
2.3 PROBLEMS WITH TRADITIONAL ACOUSTIC ANALYSIS OF VOWELS ........................... 25
2.3.1 Data acquisition problems ......................................................................... 25
2.3.2 Data acquisition with a standard analog Marantz recorder ....................... 26
2.3.3 Digital recording with a MiniDisc player ................................................. 29
2.3.4 24-bit digital recording with a close-talking microphone ......................... 31

 

 

 

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2.3.5 The role of the microphone ....................................................................... 32

2.3.6 Different signal acquisition methods return different formant values ...... 35
2.3.7 LPC analysis of nasalized vowels: Is /2e/ really raising? .......................... 41
2.4 VOWEL NASALIZATION ........................................................................................... 43
2.4.1 The velopharyngeal port ........................................................................... 43
2.4.2 Oral and nasal formants ............................................................................ 45
2.4.3 Spectral characteristics of synthetic nasalized vowels .............................. 46
2.4.4 Spectral characteristics of the oral ~ nasal contrast in Polish ................... 47
2.4.5 LPC and nasalized vowels — evidence from Polish .................................. 49
2.5 VOWEL NASALIZATION IN MICHIGAN ..................................................................... 51
2.5.1 Nasal formants appear in the spectrum of Lower Michigan vowels ........ 51
2.5.2 Why a sociophonetic aerodynamic analysis of vowel nasalization? ........ 52
2.5.3 Quantifying vowel nasalization for sociophonetic purposes .................... 53
2.6 STUDY DESIGN AND METHODS ................................................................................ 60
2.6.1 The goals ................................................................................................... 60
2.6.2 The subjects .............................................................................................. 61
2.6.3 Data collection .......................................................................................... 62
2.6.4 Why /i/ and /u/ were excluded from the study .......................................... 63
2.6.5 Data processing ......................................................................................... 64
2.7 STATISTICAL ANALYSIS OF %N ............................................................................... 65
2.7.1 %N - two-way analysis of variance .......................................................... 66
2.7.2 Follow-up test #1 — simple main effects tests ........................................... 67
2.7.3 Follow-up test #2 — pairwise comparisons ................................................ 67
2.7.4 Follow-up test #3 - interaction comparisons ............................................. 68
2.7.5 Summary of the statistical analysis of % Nasalance ................................. 68
2.7.6 Is nasalization global or local? ............................................ A ...................... 69
2.8 STATISTICAL ANALYSIS OF Al-Pl .......................................................................... 70
2.8.1 Summary of the spectral method .............................................................. 71
2.9 NASALIZATION AND NCCS ..................................................................................... 73
2.9.1 The F2 of /a/ as an index of talker participation in NCCS ....................... 74
2.9.2 Investigating the correlation of %N and Bark-transformed F2 ................ 80
2.9.3 Summary ................................................................................................... 81
CHAPTER 3 PERCEPTIONS OF /a/-FRONTING .................................................... 83
3.1 INTRODUCTION ....................................................................................................... 83
3.1.1 Introduction to talker normalization ......................................................... 83

vi

3.2 THE STUDY ............................................................................................................. 89

3.2.1 Motivation for the study ............................................................................ 89
3.2.2 Lower Michigan and the Upper Peninsula ................................................ 89
3.2.3 Talker normalization across the LM and UP dialects ............................... 92
3.2.4 The subjects .............................................................................................. 92
3.2.5 The stimuli ................................................................................................ 93
3.2.6 The experiment ....................................................................................... 102
3.2.7 Analysis and results ................................................................................ 106
3.2.8 Summary ................................................................................................. 114
CHAPTER 4 ON HEARER-MEDIATED SOUND CHANGE ................................ 116
4.] HOW DO DIALECTS DIFFER? .................................................................................. 1 16
4.1.1 Speaker-mediated sound change in dialectology .................................... 116
4.1.2 The need for a broader model of sound change ...................................... 116
4.2 NASALIZATION, /a/-FRONT1NG, AND SOUND CHANGE ............................................ 1 19
4.2.1 Articulatory variability and sound change .............................................. 119
4.2.2 Vowel nasalization and perceived height ............................................... 120
4.2.3 Hearer-mediated /a/-fronting .......................... A ........................................ 123
4.3 TOWARD A SOCIOPHONETIC MODEL OF HEARER-MEDIATED SOUND CHANGE ........ 125
4.3.1 Phonological models of nasalization ...................................................... 125
4.3.2 A sociophonetic model ........................................................................... 127
APPENDIX A - WORDLIST USED IN CHAPTER 2 ................................................... 130
APPENDIX B — QUESTIONNAIRE (CHAPTER 2 and CHAPTER 3CHAPTER 2)...131
APPENDIX C - SUMMARY OF INDIVIDUAL CASES OF %N ................................ 132
APPENDIX D -— CONSENT FORM CHAPTER 2 ......................................................... 133
APPENDIX E — CONSENT FORM CHAPTER 3 ......................................................... 134
APPENDIX F — WORDLIST USED IN CHAPTER 3 ................................................... 135
BIBLIOGRAPHY ............................................................................................................ 1 36

vii

LIST OF TABLES

Table 1. Summary of statistical results of the data acquisition test 38

Table 2. Summary of simple effect tests for men and women across the three regions and
for men and women for each region separately 67

Table 3. Summary of simple effect tests for men and women across each region
separately 68

Table 4. Summary of three tetrad comparisons to evaluate whether the differences in %N

means across the regions were the same or different for male and female respondents
68

Table 5. Overall means and standard deviations for the parameters obtained with the
Chen method 71

Table 6. LM and UP respondents’ normalized, mean F1 and FZ values in Hertz 92
Table 7. Talker LM’S and UPS mean F1 and F2 values in Hertz 100

Table 8. Mean stimulus values by stimulus type and listener region 109

Table 9. Wordlist used in studies described in CHAPTER 2 130

Table 10. Summary of individual cases for %N 132

Table 11. Wordlist used in the study in CHAPTER 3 135

viii

LIST OF FIGURES

Figure 1. American English vowels talked about in this dissertation xiii

Figure 2. Vowel quadrilateral with IPA-style phonetic symbols 7

Figure 3. NCCS vowels and their movement within the two-dimensional F 1 /F2 space 11
Figure 4. Responses to the word "pop" (From Niedzielski (1999) with permission) 12

Figure 5. F ormant tracks of the 7 variants of the male pronunciation of the word " guide"
14

Figure 6. Mean responses to male and female voices 15

Figure 7. /i/ and /I/ and /p/ and /b/ identification and discrimination results (From Pisoni
(1973) with permission) 19

Figure 8. Simplified spectrograms of the stimuli used in the experiment From Fitch, et al
(1980) 20

Figure 9. The fronting of /a/ and the raising of /a=:/ 25

Figure 10. LPC of the vowel /i/ superimposed on the noise spectrum of the Marantz
recorder 27

Figure 11. Spectrum of 60 Hz hum and the vowel /a/ in “job” 28

Figure 12 Waterfall plot of the word "hat" by an NCCS-influenced female talker 31
Figure 13 Waterfall plot of the word "hat" by an NCCS-influenced female talker 32
Figure 14. Low-end bias due to proximity effect of the AKG C-420 microphone 33
Figure 15. Frequency response of Shure Beta 87a and Earthworks M30 35

Figure 16. Box plot of overall between-subject comparisons of the data acquisition test 39

Figure 17. LPC Spectra of the /a/ vowel in "job" by a female talker with an NCCS-
influenced vowel system acquired with by different methods 41

Figure 18. Sample vowel systems of Detroit, MI females (from Labov et a1. (1997) with
permission) 42

Figure 19. Schematic view of lowering (opening) of the velum during the production of
nasalized vowels 44

ix

Figure 20. Schematic view of the oral and nasal passages (of a male talker) involved
during the production of nasalized vowels (based on Chen (1997)) 45

Figure 21 A theoretical model of the relationship between the area of the opening of the
velopharyngeal port and the frequency and amplitude of oral and nasal formant for

the vowel /2e/ (from Stevens (1998)) 46

Figure 22. Spectrum of the /ae/ vowel synthesized with the velopharyngeal port opening
of 0 m2, (left), and 7 m2, (right) 47

Figure 23 Spectrum of the /2e/ vowel synthesized with the velopharyngeal port opening of
14 mm2 (left) and 21 mm2 (right) 47

Figure 24. Spectrogram of a Polish minimal pair /kreto/~/kréto/ 48
Figure 25. Spectral characteristics of the oral ~ nasal contrast in Polish 49
Figure 26. Comparison of LPC and FFT Spectra of /e/ and /E/ 50

Figure 27 Examples of non-nasalized vowel spectra (left) and nasalized spectra (right) of
the vowel /ae/ in "back" 52

Figure 28. Waterfall plots of the word "back" of the same sample as in Figure 24 52
Figure 29. Oral and nasal prominences involved in Chen's method 54

Figure 30. Flowchart ofAkustyk's algorithm designed to automate the peak seeking
process 56

Figure 31. The Rothenberg mask 59
Figure 32. Spectra from the oral and nasal channel of /a/ in “lot" 63

Figure 33. % Nasalance means by respondent region and sex 66

Figure 34. Male and female %N distribution patterns by vowel in non-nasal environments
and for non-high vowels 70

Figure 35. Summary of A 1 -PI results obtained in the spectral method (higher A 1 -P1 =
lower nasalization) 72

Figure 36. Continuous % Nasalance levels for the words "dad" and "man" 73

Figure 37. Overview of Akustyk ’5 analysis tools 76

Figure 38. Cochleagram of /a=:/ in “back” by a female Detroiter 79

Figure 39. Scatter plot of Bark-transformed F2 and Mean % N fitted around the
regression line 81

Figure 40. Differences in F l and F2 due to VTL variability between men and women
(from the Peterson and Barney corpus (1952)) 84

Figure 41. Bark-transformed, discriminant plane Of formant values from the Peterson and
Barney corpus 86

Figure 42. Bark-transformed, discriminant plane of formant values from a corpus of 26
adult NCCS speakers 87

Figure 43. Lower Michigan and the Upper Peninsula 90
Figure 44. Normalized, mean formant values of LM and UP participants in the study 91

Figure 45. F1 and F2 tracks of the 7-Step continuum of “sock-sack” used in the
experiment 96

Figure 46. Spectrographic images of the first step and last step of the /a/~/ae/ continuum
97

Figure 47. LPC spectra of the first and last step of the continuum 97
Figure 48. Vowel systems of talkers LM and UP 100

Figure 49 Finalized stimulus: a UP or LM precursor phrase with a synthesized target th
or st word at the end 101

Figure 50. Spectrogram of a carrier phrase fragment by Talker UP 102

Figure 51. Psychometric functions of LM respondents to the stimuli with LM precursors
and UP precursors 108

Figure 52. Psychometric functions of UP respondents to the stimuli with LM precursors
and UP precursors 108

Figure 53. LM and UP responses as a function of the precursor phrase 111
Figure 54. Summary of the word effect 112

Figure 55. Precursor phrases 1 through 4 (Pl-P4) - not significant in vowel identity
judgments (From Rakerd and Plichta (2003) with permission) 113

Figure 56. Normalized Principal Components plot of an NCC S population from Lower
Michigan 122

Figure 57. Summary of the Ohalan listener-mediated model of sound change 126

xi

Figure 58. Sociophonetic model of bearer-mediated sound change 128

xii

KEY TO SYMBOLS AND ABBREVIATIONS

l. AAVE - African American Vernacular English (also known as AAE — African
American English)

2. ATRAC -— Adaptive TRansform Acoustic Coding

3. FFT — Fast Fourier Transform

4. LPC — Linear Predictive Coding.

5. NCCS — the Northern Cities Chain Shift (also known as NCS)
6. RMS - Root Mean Square

7. VOT — Voice Onset Time

8. VTL — vocal tract length

9. Vowel symbols with examples (Figure 1):

 

20 :
CD head

FI (HZ)

a :

 

1000 F i I
3150 2520 1890 1260 700
F 2 (H2)

 

 

Figure 1. American English vowels talked about in this dissertation

xiii

CHAPTER 1
INTRODUCTION

1.1 THE GOALS OF THIS DISSERTATION

Sociolinguists and dialectologists are generally interested in studying speech production
as they trace the distribution of such phenomena as the Northern Cities Chain Shift
(N CCS) through social and geographical space. More recently, they have also become
concerned with Speech perception as it enables them to investigate the effects of such
sound changes on both comprehension and attitude and, perhaps even more centrally, as
it allows them to look for the origins of sound change in Speech perception (e.g., Thomas

(2002)).

From both these points of view (production and perception), students of variation have
increasingly relied on methodologies and instrumentation of related areas of scientific
inquiry. Speech science, for example, offers a number of sophisticated practices
involving computer-assisted acoustic analysis (in studies of speech production) and
speech synthesis (in studies of speech perception). This dissertation includes two studies
based on such practices. The first, an acoustic study of vowel nasalization in an on-going
sound change (NCCS), suggests that previous accounts of NCC S can be broadened by an
aerodynamic study of oral and nasal airflow, and that vowel nasalization plays a

considerable role in the re-shaping of the NCCS-influenced vowel space. The second

study provides a perceptual account the /a/~/ae/ category boundary shift in the F2 domain

(e.g., “hot”~”hat”) as a function of talker participation in NCCS. Finally, the two studies

will come together in an argument of sociophonetic hearer-mediated sound change.

1.2 VARIA TIONIST SOCIOPHONETICS

In the 19605 and 70s sociolinguists began adopting the notion that there are sources of
variability in language that can be traced to specific extralinguistic, social factors such as
class, gender, race, and age. Following the results of his study of copula deletion in
African American Vernacular English (AAVE), Labov (1969) suggested that these
sources of variability can be quantified by means of the so-called “variable rule.” He
annotated variable rules with probabilistic weights indicating how much a condition
favors or disfavors the application of the rule. Subsequently, these conditions were
expanded to include a wide range of social factors. Soon, Labov’s methodology provided
the theoretical foundation for the branch of linguistics known as variationist

sociolinguistics.

Since the early days of the discipline, many studies have focused on phonological
variability (e.g., Labov (1972)) in phenomena such as deletion, epenthesis, devoicing,
and other acoustic events perceivable by a trained phonetician. Later, researchers realized
that in order to pursue this analysis to deeper levels, they had to study lower-level
phonetic detail as well. While many sociolinguists still relied on impressionistic
judgments of allophonic-level phonetic quality, a growing number of researchers
employed acoustic analysis of speech, which enabled them to find patterns and

relationships beyond those available with traditional methodology and instrumentation.

For example, Eckert (1999) explored the variability in the production of the second

formant Of /A/ as a marker of group identity. Bailey and Thomas (1998) provided a

detailed acoustic account of AAE (African American English), while Preston, Evans, Ito,

and Jones (2000) explored the Northern Cities Chain Shift (NCCS) in Michigan from an
acoustical point of view. As a result of this in-depth pursuit of acoustic detail, a new sub-

discipline of sociolinguistics known as “sociophonetics” has emerged.

1.2.1 This dissertation as a work in variationist sociophonetics

This dissertation is a work in sociophonetics. It aims at discovering, analyzing, and
explaining low-level phonetic detail and its correlation with social factors such as sex,
and region, both in terms of speech production and perception. It employs rigorous
methodologies in the process of data acquisition and analysis. The acoustic features under
investigation are most probably beyond the talkers’ linguistic awareness, which makes
the data particularly interesting, as our understanding of the role of low-level phonetic
detail brings us one step closer to understanding the mechanisms of language variation

and change.

This dissertation is also a work of discovery. With few theory-dependent assumptions, it
demonstrates that there exist certain, previously unexplored. sources of sociophonetic
variability. It also shows that this variability is not random. On the contrary, it is

systematic in ways consistent with milestone works in variationist sociolinguistics.

1.2.2 Current state of the field of sociophonetics

1.2.2.1 Speech production

There is no doubt that sociophonetics has been dominated by empirical studies of speech
production. Ranging from small studies Of local variation (e.g., Anderson (2002)) to large

projects such as the Telsur Project (Labov, Ash, & Boberg, 1997), there have been

numerous studies of vowel production, many of which used computer-assisted acoustic
analysis. Sociolinguists have acquired a substantial knowledge of American English
vowel systems, at least within the two-dimensional space delimited by the first and
second formant (see 1.3.2.1). What still remains largely unknown, however, is how other
articulatory-acoustic features, such as vowel nasalization fit in with the existing

sociolinguistic research paradigm.

1.2.2.2 Speech perception

Sociophonetic speech perception studies are still rare. One of the first rigorous and
methodologically advanced studies in sociophonetic speech perception was a study by

Grafi‘, Labov, et al (1983). Using digitally manipulated speech samples (the onset of the

diphthong /aU/) they were able to Obtain different judgments of vowel quality across

ethnic lines. However, as Preston (1999) notes “such specific experimental procedures do
not seem to be common in this research paradigm” (p. 7) that is at the interface Of
sociophonetics and speech science. As will become apparent later in this dissertation, this
is not to be confused with perceptual dialectology, which has been very prolific, and

which Often provides inspiration for more phonetically focused, experimental studies.

1.3 SOCIOPHONETIC STUDIES OF SPEECH PRODUCTION
1.3.1 Quantitative studies of consonantal variation

1.3.1.1 Impressionistic studies and feature dichotomy

Sociophonetic studies of speech production attempt to find phonetic correlates of socially
constructed linguistic behavior, and segmental features have been the most common

object of acoustic scrutiny. Labov (1972) investigated the distribution of /r/ in data

collected in three New York department stores. This classic study of consonant deletion
relied on impressionistic judgment of the presence or absence of /r/ in stressed syllables
(e.g., “fourth,” “floor”). Labov examined a broad range of predictor variables, such as
social class and gender, to establish their correlation with a particular phonetic (or

allophonic) variant of /r/.

Many similar studies have been done since. In a typical case (e.g., Guy (1980)), the
investigator collects pronunciations of target tokens as well as extralinguistic information
about the speaker and the circumstances in which the utterance was made (metadata). The
dependent variable is usually dichotomous (e.g., glottalization of stop consonants in word
final positions) and its value depends on the investigator’s impressionistic judgment.
Logistic regression analysis is often employed to explore possible relationships across the

variables (Sankoff, Rand, Rousseau, Hindle, & Pintzuk, 2004).

1.3.1.2 Limitations of impressionistic studies based on feature dichotomy

The limitations of such a design are twofold. First, the value of the dependent variable is
assigned by the researcher’s auditory impression of the token. While many phonological
theories assume the binary nature of contrastive sounds (e.g., Chomsky & Halle (1968)),
modern theories of speech production claim that the speech signal consists of
continuously time-varying frequency components (e.g., Stevens (1972)). Human speech
processing works on at least two levels — across category, sometimes referred to as
contrastive or phonemic, and within category, also known as non-contrastive or
allophonic. While listeners have generally no difficulty decoding the contrastive building

blocks of speech, they can have great difficulty consciously evaluating variable sounds

within a single phonemic category (Liberman & Mattingly, 1985). Thus, proper
identification and classification of those low-level, articulatory and acoustic details, even

by a trained phonetician, is prone to be inaccurate.

The other problem with this kind of design follows directly from the principles of speech
production and perception described above. A number of phonetic features are, by nature,
continuous. Voice onset time, formant transitions, release burst, intensity, or
coarticulatory assimilation are best represented on an interval, rather than categorical
scale (e.g., the presence vs. absence of a feature). While binary logistic regression can
produce very robust results (Paolillo, 2001), the reduction of continuous criterion

variables to a binary form may lead to imprecise prediction claims.

1.3.2 Quantitative studies of vocalic variation

1.3.2.1 The vowel quadrilateral as a metaphor for vowel space and movement

Sociophonetic investigation of vowel production has produced impressive results. Since
Labov Yeager, and Steiner (1972) introduced acoustic analysis to dialectal variation,
sociolinguists have increasingly utilized instrumental techniques. Many American
dialects (including NCCS) have been described in phonetic terms. The Atlas of North
American English is one of the most influential and largest sociophonetic projects in
modern dialectology. Labov and his colleagues (1997) collected speech samples by
phone and analyzed them acoustically. The first and second formant of the steady state of
each vowel and diphthong were recorded and plotted against a perceptual vowel
quadrilateral, first proposed by Jones (1964) and later adopted by Ladefoged (1967)

(Figure 2).

1 O y ............................................... 1.3 m . u
I Y ' U

Fl
0.)

Figure 2. Vowel quadrilateral with IPA-style phonetic symbols

The F1/F2 plot with reversed axes (values increase right-to-left and top-to—bottom — see

the arrows labeled “F 1 ” and “F2” in Figure 2) corresponds closely to the perceptual

space proposed by Jones. For example, the high front tongue position for /i/ matches the
low F1 and high F2 values of /i/. This representation of vowel Space has given rise to an

entire tradition of vocalic variation analysis in sociophonetics. The major premise of
NCCS, for instance, is that vowels move is this space along the height (F 1) and frontness
(F2) dimensions. For instance, Preston and Evans (2000) created a degree-of-shift scale

that they used to determine the degree to which talkers participated in NCCS. The scale

was based on F1 and F2 differences in Hz, but spatial relations such as “higher than /8/”

were used to great explanatory effect.

1.3.2.2 Beyond F1 and F2

The two-dimensional sociophonetic analysis of vowels has proved successful in
establishing general dialectal boundaries (isoglosses) (e.g., Fridland (1998)). However,
this type of analysis is not without limitations. For instance, it disregards a good deal of
Spectral information, such as formant bandwidths, F0, or the third formant frequency
(Thomas, 2000). It also does not respond to current theories of speech perception, which
emphasize the importance of critical band analysis in auditory processing (Chistovich,
1985), and contextual factors that may influence perceptual decision-making (Johnson,
1989). There is no doubt that much remains to be discovered by studying other acoustic
phonetic features of vowels. As will be seen later in this work, aerodynamic studies hold
a promise of interesting, new findings, as do quantificationa] studies of formant

trajectories.

1.3.3 Sociophonetic accounts of suprasegmental features

Segmental variation is not the only object of sociophonetic scrutiny. Perceptual and
attitudinal studies, such as Preston (1996), have demonstrated that listeners often
categorize dialects and sounds with descriptive terms such as “fast,” “slow,” “nasal,”
“drawl,” or “twang,” which seems to extend folk linguistic perceptions well beyond
phonetic segments. Sociolinguists often try to translate these intuitive labels into
quantifiable suprasegmental features of voice production. However, while many listeners
have little trouble identifying stereotypical dialectal differences based on intonation,
pitch, rhythm, and phonation characteristics, these features still seem illusive to

sociophoneticians (Thomas, 2000).

One of the most serious Obstacles in sociophonetic research of suprasegmental variation
is a general difficulty in quantifying suprasegmental events. Intonation, for instance, is
variable within a broad range of frequencies over time. This variability is correlated with
a number of linguistic and extralinguistic behaviors, such as semantics, style, register, or
emotion. Despite the existence of notation systems. such as TOBE (Beckman &
Hirschberg, 1994), quantificational sociophonetic work in suprasegmental features of

speech production has not been nearly as extensive as the work on segments.

Several notable exceptions in contrastive dialectology include the work of Be Ling,
Grabe, and Nolan (2000) whose findings provide acoustic data (duration and F1/F2 space
characteristics) to support the phonologically hypothesized differences in rhythm

patterning between British English and Singapore English.

1.4 SOCIOPHONE TIC STUDIES OF SPEECH PERCEPTION

1.4.1 Perceptual dialectology

There has been a great deal of research in the area of perceptual, or folk dialectology
(e.g., Preston (1996)). Perceptual dialectology is primarily concerned with common (folk)
perceptions of language and language varieties. It is most interested in the relationship
between language (or dialect) and various social constructs associated with it, such as
identity, regionalism, standardness, gender, and ethnicity. In their attempt to study the
nature of such beliefs and attitudes, researchers have often crossed over into other areas
of scientific inquiry, such as social psychology (Williams, 1976), cultural geography
(Preston, 1996), speech science (Pumell, Idsardi, & Baugh, 1999), and cognitive science

(Nussbaum & Morin, 1993). The results of perceptual research have shown that

perceivers generally hold strong stereotypical views of dialects and their speakers, and

that those views may have a considerable influence on speech perception.

1.4.2 Experimental studies of speech perception

Sociophonetic studies Of Speech perception have been less common than both perceptual
dialectology and production studies. Thomas (2002) attributes this to the lack of
technological expertise that is necessary to design and carry out perceptual experiments.
However, some of the recent results of perceptual research have encouraged

sociophoneticians to venture into the world of speech science.

1.4.2.1 Social information and speech perception

Niedzielski (1999), in a sociophonetic speech perception study, found that judgments of
vowel quality may be influenced by nationality labels. She presented 41 Detroit area

residents with a set of “re-synthesized” stimuli.

The subjects were asked to choose from a set of re-synthesized variants the one that best
matched the vowel heard in the tape-recorded speech sample of a fellow Detroiter. Half
the respondents were told that the speaker they heard on the tape was from Detroit, and

half were told that he was from Canada. In the first experiment involving the word

“house”, and more specifically, the diphthong /aU/, nationality labels significantly

influenced the pattern matching task.

The remaining two experiments, involving the words “pop” and “last” did not obtain

significant results in the same way. However, these two experiments demonstrated a

10

really interesting phenomenon. The word “pop” contains the vowel /a/, which is one of

the vowels that are believed to be participating in the Northern Cities Chain Shift (N CCS)
(Labov et al., 1972). Figure 3 shows the way in which the vowels are supposed to be

moving within the two—dimensional Space.

 

 

I
I
I
l
I
I
I
I
I
I
I
I
I
. 3
I
I
I
I
I
I
I
I
I
I
I
I
I

as. 2 a
Figure 3. NCCS vowels and their movement within the two-dimensional F1/F2 space

Detroit residents, no doubt, have been exposed to NCCS-influenced speech. It is likely
that they themselves also sound this way. Yet, when presented with re-synthesized

samples of the word “pop” they overwhelmingly choose a synthetic “canonical” version
of /a/, in preference to a version that mirrored Detroiters’ productions. “Canonical” is the

term Niedzielski used to describe vowels whose F1 and F2 were similar to those found

by Peterson and Barney (1952). Figure 4 Shows the response patterns.

Niedzielski’s account of this phenomenon is consistent with those found in perceptual
dialectology, such as Preston (1999). Niedzielski argues that Detroiters consider
themselves to be speakers of Standard English and, therefore, are reluctant to recognize

an NCCS-influenced vowel as similar to their own. Instead, they select the canonical

ll

form, demonstrating that their perception of a vowel variant within the same linguistic

category (or phoneme) is sensitive to changes of 200 Hz along F 1 and F2.

100.0

 

 

P0P

 

= 80.0 .
60.0 -
40.0 1

% of vowel variant chosen in

1:1 Canadian
. 1:] Michigan

 

 

 

 

 

 

 

        

fits-3'13" -

0.0 . .
Actual token

 

Hyper-standard hCanonical
Variant of /a/

Figure 4. Responses to the word "pop" (From Niedzielski (1999) with permission)

1.4.2.2 Vowel quality and vowel identity: “within category” and “across category”
speech perception

The distinction between studying vowel quality and vowel identity is important to the
understanding of this dissertation. Niedzielski (1999) studied vowel quality. Each time
the respondent was presented with a stimulus, it was known which word (and hence
which vowel) would occur. The listeners’ choice was about a preferred token (a variant)

Of the vowel type.

Alternatively, acoustic cues might be varied over a range that causes a perceived change
in vowel identity, that is a perceptual shifi from one phonemic (contrastive) category to

another (Stevens. 1985).

12

1.4.2.3 Vowel quality and dialectal stereotype

It is no secret that listeners have a certain sense of regional dialect. However, what still
remains illusive is which specific pronunciation features contribute to regional dialect

identification. Within category vowel variability is a mainstay of sociophonetics, and the

pronunciation of /a1/_. for example, is one of the more interesting such features. Plichta

and Preston (2004) presented 96 respondents from different parts of the United States
with a task of matching a variant of the word “guide,” spoken by a male and a female
talker, with one of nine geographical locations along the North-South dimension from

Saginaw, M1 to Dothan, AL (the stimuli were randomized and repeated four times each).

They used the Linear Predictive Coding (LPC) analysis/re-synthesis method (see also
3.2.5.1) to produce a 7-step continuum of the word “guide” by Simultaneously varying
the first formant (“raising”) by a total of 150 Hz and the second formant (“fronting”) by a
total of 550 Hz (see also 3.2.5.2). The female and male pronunciations remained parallel
in how their formant trajectories changed throughout these continua. Figure 5 Shows
formant tracks of F1 and F2 at each of the seven steps. The dots of each shade of gray
represent formant trajectories at each of the seven steps of monophthongization and

fronting.

13

 

3000

 

 

 

‘0
.......... .. . . 0 t. 0 0. ..I
3? .
f5 Frontmg
>5 2000,...“ .. .. .
U . .....
5 0:: ....... ..
:3 '8! 15h” .........
o- ng'Ht. ;- .m' -- ....... I
cg "'3! .:?li'il.! ”H” o...
E . .
g MonophthongIzation
LL.
..... 0000...... . ...: ......
" ":Hiiiiiftzxx'
I38£§;;..
.0
0
0 . 0.7199
TIme(S)

Figure 5. Formant tracks of the 7 variants of the male pronunciation of the word "guide"

This study is similar to that of Niedzielski (1999) in that it also focuses on “within

category” perception. Despite the substantial overall changes in F 1 and F2 frequencies

between the first and the last step, the sound /aI/ retained its contrastive (phonemic)

identity. However, despite relatively small differences in F l and F2 frequencies between
the neighboring steps of the continuum (less than 30 Hz along F 1 and 60 Hz along F2),
listeners responded differently to each stimulus. The responses changed over the series in
a nearly continuous manner, as if mapping the acoustic continuum onto the geographical
one. Figure 6 shows mean responses to the male and female voices. Each stet (except step

1 and 2) is significantly different from its neighbor.

l4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

North 9
7 .l 6
.E 5 99 i
E 4.5
5 .--i . 1‘ J i
g 3 .6 9 ‘.3__;:i ‘ 1‘
"E is"‘* i ' | ' “H ti. .
2 i Subject sex
Fist L”, B Female
4%}... .. ,
$.33}:- ; Male
I
South 0 ---,.,_ - I , ..« 1 1 _ . l . f

Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7
Monophthongization steps

Figure 6. Mean responses to male and female voices

As evident in Figure 6, the female voice (white bars) was not rated as “Southern” as the
male voice (gray bars). This, along with the overall sOund-region mapping result,
contributes to the general conclusion that, in experimental conditions, listeners are
capable of distinguishing among very small absolute changes in the frequency domain of
the first and second formant, even “within category.” Moreover, this perception is
systematic and consistent with the common stereotypes of Southern and Northern speech.
Niedzielski’s study demonstrated that changes of 200Hz in the first and second formant
frequencies carry dialectally salient weight; this study showed that even smaller

increments in formant frequencies play a role in regional dialect identification.

1.4.2.4 Gender information and speech perception

Strand and Johnson (1996) were interested in “across category” perceptual effects, that is
in identity shifts. They studied the effects of gender on speech perception and

demonstrated that perceivers produced differing consonant identity responses to the same

15

stimuli (/s/ and /j/) depending on the perceived gender of the talker. This can be

explained by the fact that men have lower turbulence frequency in these consonants than
women. The authors suggest that that the bottom-up processing of acoustic information
interacts with higher-level information related to socially constructed stereotypes about
gender. Moreover, this process happens across two modalities — the auditory and the
visual. What is of most consequence to this dissertation is the claim that the perceived
information about the talker’s gender, along with the pre-conceived, and implicit
knowledge of men sounding different than women, appears to be tightly integrated in the

speech perception process.

1.4.3 Needed research in perceptual sociophonetics

Though interest in the sociolinguistic content of Speech Signals appears to be growing,
sociophonetic perception studies are still rare. Perhaps the most intriguing questions that
perception studies might answer are (1) how much sociolinguistic information is
conveyed in the speech signal (bottom-up information), and (2) to what extent external
knowledge and expectations (top-down information) influence speech perception. Better
understanding of such processes can help solve the age-old mystery of sociolinguistic
salience. In addition, sociophonetic research in speech perception can play a crucial role

in broadening our understanding of role of sociolinguistic information in the talker

l6

normalization process.I Thomas (2002) points out that speech perception is likely to

continue having an increasingly important role in sociophonetic research.

1.4.4 Speech Science -similar questions, different methodologies

1.4.4.1 Introduction to perceptual research in speech science

Investigations of speech production and perception have been of interest to speech
scientists for some time. In the 19503, when the first speech synthesizers and
spectrographic speech analyzers became available, speech scientists obtained a great deal
of new insight into the details of speech production and perception. Several theories of
speech perception have evolved based on this work These include the Motor Theory
(Liberman & Mattingly, 1985), Quanta] Theory (Stevens, 1989), and the Fuzzy Logical
Perception Model (Massaro, 1998). Just as sociolinguists recognize the need to venture
into speech science, Speech scientists acknowledge the importance of integrating the

sociolinguistic dimension in their work (Nussbaum & Morin. 1993).

This interest was first sparked by Ladefoged and Broadbent (1957)2 who demonstrated
that the final output of the perception process is contingent upon the integration of
sensory input with a (more abstract) vocalic frame of reference. Details of this study are
summarized in 3.1.1.5. More recently, Purnell and Kopplin (2003) investigated the role
of source and filter characteristics in the identification of talker ethnicity. They found that
filter characteristics (formant values) play a crucial role in distinguishing between

General American English and African American English.

 

' See discussion of the Ladefoged and Broadbent (Ladefoged & Broadbent, 1957) study in CHAPTER 3.
2 See 3.] for a more detailed discussion.

17

1.4.4.2 Dynamic constancy

An influential theory of speech perception argues that discreet units of speech, Often
referred to as phonemes, are encoded by the talker into the highly variable speech signal.
These units are then decoded by the listener, effectively correcting for the
articulatory/acoustic variability and separating linguistic elements from noise (Liberman

& Mattingly, 1985).

This phenomenon, Often referred to as “dynamic constancy,” bears close resemblance to
the variable rule theory of variationist sociolinguists. One might even think of those two
fields as complementary since one deals with physiological and the other with social
correlates of human speech. However, the seemingly simple marriage of those two ideas

has not yet been widely acknowledged.

1.4.4.3 Categorical speech perception

As mentioned in 1.4.2.2, speech perception studies can investigate “within category” or
“across category” effects. Speech scientists have been most preoccupied with “across
category” effects. In the 1950s, perceptual research revealed that the perception of speech
sounds is not linear and that it differs greatly from the perception of other sounds in the
time/frequency domain.3 Specifically, continuous acoustical changes in the auditory input
can result in sharply discontinuous, categorical perceptual shifts. There have been many
categorical perception studies, but there is one that is of most importance to this

dissertation (also see 3.2.7 later in CHAPTER 3).

 

3 Such non-Speech sounds are sometimes referred to as “non-speech analogs.” The perception of “within
category” human sounds have been very similar to that of non-speech analogs.

18

Pisoni (1973) performed a series of perceptual tests with vowels of varying length (300
ms and 50 ms) and with consonants of varying voice onset time (/p/~/b/). He discovered a
very categorical perception of consonantal contrast and a Slightly less categorical
perception of vowels. However, as vowels became longer, their perception patterns
became similar to those of consonants. This was also the case when there was an extra
stimulus presented between subsequent vowel tokens. Figure 7 shows psychometric
functions from the mean aggregate responses to long vowels and VOT variability in
/p/~/b/ pairs. The vowel identification task (left) produced results similar to those of
voicing identification. Even though the stimuli increase incrementally and steadily in

equal physical steps, the change in auditory response occurs rapidly and discontinuously -

when the stimulus reaches the area close to the category boundary of /i/~/I/ and /p/~/b/,

respectively. The fiInctionS of the /p/~/b/ contrast appear to have slightly steeper slopes,

which indicates a more categorical perceptual process (see also 3.2.7.1).

Bilibial slop
/i/ Identification /I/ lb/ voicing ’Ip’,

]00, o--. 100-

75» 75~

 

 

 

 

 

‘5 .-
P :4
t: d
.3 S -
z 50 I- 'J 30 '-
5 E
a 3
a E
25 p— 25 l-
0 '- ‘.--‘--.. 0+— ‘.-"'.’“.
1 l l 1 1 l I J. 1 1 l 1 l l
l 2 3 4 5 6 7 l 2 3 4 5 6 7
Stimulus value Stimulus value

Figure 7. /i/ and /I/ and /p/ and /b/ identification and discrimination results (From Pisoni
(1973) with permission)

l9

1.4.4.4 Integration of acoustic cues in speech perception

In a classic stimulus integration study. Fitch, et a1 (1980) discovered that the perception
of category (or phoneme) boundaries shifts as a function of the consonantal environment
in which they occur. Two “Slit”~“split” continua were synthesized with varying amounts
of silence between the high frequency noise of /S/ and the onset of the voiced remainder

of the words (/lIt/), as can be seen in Figure 8. The results of the experiment showed that

the boundary between “split” and “Slit” changed depending on the length of the silence
interval. The longer the interval (right), the more likely were the respondents to identify
the stimulus as “split,” and vice-versa. This change of category boundary (or phonemic
boundary) depending on external factors (in this case the duration of the silence interval)

was termed “phonetic trading relations.”

 

 

 

 

 

: : : vocalic section 1 . vocalic section

1: [In] 2 [pm]

Li if

l O I , 0

1'; I I ,7;
'>,. g '—. Formant 3 : 5 PI
E .g . : E
U. . Formant 2 f
E

r“ Formant 1 F

 

 

Time ——>

 

 

 

Figure 8. Simplified spectrograms of the stimuli used in the experiment From Fitch, et a1
(1980)

20

1.5 ORGANIZATION

The present dissertation applies technological and theoretical advances from speech
science to the study of sociophonetics — specifically to the study of NCCS. It includes

two studies, one is speech production, and the other is speech perception.

1.5.1 The speech production study

The speech production study presented in CHAPTER 2 discusses some problems with the
methodologies and instrumentation of previous vowel analyses and presents a new
method of sociophonetic analysis of NCCS-influenced vowels. A new research
methodology is also proposed. It is based on aerodynamic measurements of vowel
nasalization and it investigates the distribution of vowel nasalization as a sociolinguistic
marker. This method is meant to provide a tool, complementary to current approaches, to

further our understanding of this intriguing vowel shift.

1.5.2 The speech perception study

The speech perception study in presented in CHAPTER 3, focuses on “across category”

perceptual influences of dialectal information conveyed by the speech signal. It

demonstrates that the fronting of /a/, one of the markers of NCCS (Labov et al., 1972),

bears a significant amount of perceptual weight among NCCS speakers, which, in turn,
provides experimental validity to previous studies in perceptual dialectology, such as

Labov (1991), which reported a perceptual confusion between /a/ and /aa/ between NCCS

and non-NCCS dialect speakers.

21

1.5.3 Comments and conclusions

The last chapter brings the two studies together into a unified account of bearer-mediated

sound change. It will be argued that the re-Shaping of the NCC S vowel Space is correlated

with vowel nasalization and that the movements of /m/ and /e/. in particular, result from

an on-going speaker-bearer negotiation of vowel contrast influenced by nasalization.

Similarly. the salience of /a/-fronting is negotiated via the hearer’s speech community.

One’s speech community must be perceptually ready before it can reproduce and spread

new pronunciations of /a/.

22

CHAPTER 2
VOWEL NASALIZATION AND NCCS

2. 1 INTRODUCTION

This chapter presents an analysis of sociophonetic variation in Michigan. The analysis
goes beyond the tWo-dimensional formant frequency measurements associated with the
vowel quadrilateral. The specific goal of this chapter is to demonstrate, with quantitative
analyses, that new insight into sociophonetic variation can be gained from aerodynamic

measurements Of oral and nasal airflow.

In particular, the study will Show that the output of traditional computer-assisted
sociophonetic analysis of vowels is subject to significant variability depending on data
acquisition techniques used, and that this variability is particularly high in the LPC

measurements associated with F 1. The problem becomes even more serious when
applying standard LPC analysis to vowels with complex spectra, such as nasalized /aE/.
Nasalized /26/ is particularly difficult because a low-frequency nasal formant, typically,

comes below the first oral formant, thus Often forcing the LPC filter into returning a low-

frequency value misinterpreted as the first oral formant of /m/. If this technique is applied

to a large speech corpus, subsequent statistical analysis may lead to a significant biasing

of the data and to a misclassification of /ze/ as “raised” (marked by lower F 1 frequency)

when /a:/ is in fact nasalized.

The study will further Show that vowels pronounced by many Michiganders have rather

complex Spectral features, and that they often show the presence of nasal peaks and anti-

23

formants. Moreover, these nasal features occur in nasal, as well as non-nasal
environments and are distributed along sociophonetic isoglosses of region and sex.
Finally, quantificational evidence will be provided to support the claim that the amount of
nasalization in non-nasal environments (e.g., “dad’_) is correlated with talker participation

in NCCS.

2.2 FRONTING AND RAISING

As mentioned in 1.4.2.1, NCCS is defined as a movement of vowels in the two-
dimensional space delimited by F1 and F2 (see Figure 3 in 1.4.2.1). This has given rise to
the nomenclature of physical movement, which is often used in sociophonetics (Preston
et al., 2000). For example, if a vowel is said to “front,” it is to be interpreted that the F2

of this vowel increases. Similarly, if a vowel is said to “raise,” it is because its F1

decreases. Figure 9 provides a visualization Of this relationship. The vowel /ae/ is
“raising,” i.e., its F1 is decreasing, while the vowel /a/ is “fronting,” i.e., its F2 is

increasing. Note that the exact path of F1 in Figure 8 is not perfectly vertical, as /a/-

raising in NCCS is often believed to have a fronting component, as well (Gordon, 1997).

This framework assumes that there once was a different vowel system in Lower
Michigan and that this vowel system is now in flux, whereby vowels are “moving,”
occupying new spots and stimulating further movement of other vowels in the vowel

quadrilateral. Moreover, these movements are conjectured to be happening in a specific

order, the vowel /a/ being the first to move, though this particular process has been

disputed by diachronic research (Stockwell & Minkova, 1997).

24

20

 

400. ...... . ..... ................... ................... ...... ,,,,,, e .............. ..................................

 

 

fronting a

 

 

1000 I I I
3150 2520 1890 1260 700
F 2 (Hz)

 

Figure 9. The fronting of /a/ and the raising of /m/

2.3 PROBLEMS WITH TRADITIONAL ACOUSTIC ANALYSIS OF VOWELS

2.3.1 Data acquisition problems

It is conjectured here that data acquisition methods may have significantly influenced
past acoustic analyses of NCCS. An experiment was conducted to see how three different
field recording methods would influence acoustic analysis. The first two methods are
very common in sociophonetic literature: (1) signal acquisition with a Marantz (or
comparable) cassette recorder with a built-in microphone; or (2) recording speech with a
MiniDisc recorder and an omni-directional lavalier microphone. The third method, still
rather uncommon, involves (3) recording speech with a head-set, flat response

microphone and a 24-bit digital recorder.

25

2.3.2 Data acquisition with a standard analog Marantz recorder

Marantz portable cassette recorders have been used in the field for some time. They have
a reputation for being sturdy and for producing high quality recordings, particularly for
the purposes of news gathering and reporting. The recorder comes with a built-in
condenser microphone, capable of capturing broadcast quality sound. Note, however, that
audio quality understood in audiophile terms (e.g., “broadcast quality”) does not directly
correlate with audio quality in acoustic-phonetic terms, as the former relies on subjective
assessment of abstract sound properties, such as “clarity,” “brightness,” or “presence,”
(Barlett & Barlett, 1998) while the latter refers strictly to the acoustic fidelity and detail

of acquired sounds.

The potential problems with the Marantz-type recorder are three-fold. First, the omni-
directional microphone is too close to the recorder’s motor and tape transport mechanism.

It therefore “picks up” a lot of low frequency noise. Figure 10 shows an LPC spectrum of
the vowel /i/ superimposed on a narrow-band spectrum Of the noise produced by the
recorder.4 It is evident that the spectral band of the first formant (labeled “F 1 ”) is similar

in level to the low-frequency components of the noise (labeled “Noise”). This may

negatively influence LPC-based formant extraction.

 

‘ Other low-frequency noise generated by a computer fan or an air conditioner would have a very similar
spectrum.

26

 

Sound pressure level (dB/Hz)

 

 

 

 

0 750 1 500 3000
Frequency (Hz)

Figure 10. LPC of the vowel /i/ superimposed on the noise spectrum of the Marantz
recorder

Second, the Marantz microphone, due to its omni-directional polar pattern, records noise
from the environment, as well as the intended speech signal. If the interview is conducted
near an air conditioner, Open window, refrigerator, or any other source of low frequency
energy, the amount of noise recorded on tape can jeopardize the reliability of acoustic
analysis. Finally, this kind of microphone cannot be placed close to the talker’s lips,
which causes a great deal of attenuation (decrease in amplitude) of the speech signal

before it reaches the microphone.

2.3.2.1 60 Hz hum

Sixty Hz hum from power circuits is another common source of noise in field recordings,
particularly when unbalanced microphone cables and non-grounded power cables are

used. Figure 11 shows two spectra — one of the 60 Hz hum (solid line) and one of the

27

vowel /a/ in “job” (dashed line). The hum interferes with the speech signal in ways

similar to those illustrated in Figure 10. This problem is not unique to Marantz recorders,
though the popular PMD 101 model does not have a balanced microphone input and its
power supply is, typically, not grounded, which makes this recorder particularly
susceptible to 60 Hz hum. Also, often, in respondents’ kitchens and living rooms,
grounded power outlets are not available, and the existing wiring used for refrigerators,
TVS, air conditioners, and so forth, can potentially cause a great deal of interference.
Therefore, the use of professional-grade, balanced, properly grounded, “XLR” (also
known as “Cannon”) microphone interfaces and cables is necessary to ensure the least

amount of electrical interference.

74.5.3

 

 

Sound pressure level (dB / Hz)

 

 

 

 

 

 

 

— 60 Hz hum
---- /a/ in "job"
14.53 i ‘. ; V
0 60 166.667 333.333 500

Frequency (Hz)
Figure 11. Spectrum of 60 Hz hum and the vowel /a/ in “job”

28

2.3.3 Digital recording with a MiniDisc player

The Sony MiniDisc form factor first appeared on the market in Japan in 1992. Soon
afterwards, due to an effective 1996 advertising campaign entitled “Where the Music
Takes You“, the MiniDisc became the most popular portable consumer digital music
system (Hunt, 1996). It was also embraced by the fieldworker community, despite the
fact that it was never designed to be a quality recording device. Sony designed the
MiniDisc recorder as an inexpensive digital option to its Walkman series Of cassette

players. The MiniDisc has at least three serious problems.

First, the speech signal is altered by the recorder (compressed) in ways over which the
researcher has no control. MiniDisc recorders use a lossy psychoacoustic data
compression format called ATRAC (Adaptive TRansform AcOustic Coding). Soon after
the recorder captures and quantizes an acoustic signal, it converts it to the proprietary
ATRAC format at the bit rate of 292 kbps (approximately 1/5 of the uncompressed PCM
“CD quality” of 1.41 Mbps). All of the acoustic field data are processed by the algorithm,
which is based on psychoacoustic principles whereby the signal is divided into three
frequency sub-bands with different data reduction schemes involved in each of them
(Pohlmann, 2000). This indicates non-linear digital signal processing whereby the
original speech signal is altered in ways determined but by the best compression
(compromise between “quality” and bit-rate) options dynamically selected by the

algorithm.

Second, the standard MiniDisc recorder does not have a professional microphone

interface (see 2.3.2.1 for a discussion on the importance of the balanced XLR microphone

29

interface). As a result, only a small number of amateur quality microphones can be used
with it. It is equipped with an electret-condenser interface with so-called “plug-in power.”
Some Sony microphones are compatible with it, as are a few of other brands (e.g., Audio-
technica AT803b and AT822). While it is theoretically possible to connect a
professional-grade microphone to the MiniDisc player, this is quite difficult, as it requires

a special impedance matching in-line transformer.

Finally, an omni-directional, low—quality lavalier microphone that is most typically used
with MiniDisc players, produces a noisy recording. Figure 12 shows a waterfall
spectrogram of the word “hat” recorded with a MiniDisc recorder and an Audio-technica
AT803b microphone. The spectrum appears to contain a great deal of extraneous noise
and the formant peaks have unusually low intensity and wide bandwidths when recorded
with MiniDisc hardware. MiniDisc recordings are digitized at a dynamically variable bit
rate, but they can be converted to a PCM format at 44,100 Hz. The spectrogram in Figure
12, therefore, does contain acoustic information well over 3,000 Hz. However, this

information contains very little Spectral detail of interest to phoneticians.

30

 

Figure 12 Waterfall plot of the word "hat" by an NCCS-influenced female talker

2.3.4 24-bit digital recording with a close-talking microphone

The third scenario involves a high quality, 24-bit, 48,000 Hz digital hard disc recorder
with a flat-response, head-set microphone (Sennheiser HMD25-l). The Sennheiser
microphone has been specially designed for recording speech in noisy conditions. It has a
directional polar pattern, but despite being a close-talking microphone, it retains a flat
response throughout the entire frequency range of up to 16,000 Hz. By being close to the
talker’s lips, it records speech with a more favorable signal-to-noise (S/N) ratio. The
digital recorder, which connects directly to a laptop computer via the USB bus, is
equipped with two professional-grade microphone inputs. It reproduces pure 24-bit
sound, which contains more acoustic detail and less quantization noise than signals

captured with 16-bit digital recorders (such as Tascam DA—Pl portable DAT recorder).

Figure 13 shows a waterfall spectrogram of the word “hat” recorded by the same speaker

(see also similar plots in Figure 28), at the same time as the sample in Figure 12. The

31

complex spectral features of this vowel are more visibly strong and well defined; there is
virtually no unwanted noise. It should come as no surprise that Fast Fourier Transform
(FFT) can produce different spectral representations of what are all productions of the
same vowel captured with these three different recording methods. Similarly. Linear
Predictive Coding (LPC) analysis. which is the standard way of extracting formant values

in sociophonetic analysis, can be expected to output significantly different values (see

2.3.6 for a detailed discussion).

      

300 [115
200
1 00

Figure 13 Waterfall plot of the word "hat" by an NCCS-influenced female talker

2.3.5 The role of the microphone

AS mentioned in 2.3.2, 2.3.3, and 2.3.4, the role of the microphone in acquiring reliable
speech Signals is at least as important as the role of the recording device and the
recording medium. The ideal microphone must have a wide and flat frequency response,

and must not cause an increase in low-frequency amplitudes when placed close to the

talker’s lips (so-called “proximity effect”). Proximity effect is a natural consequence of

32

placing a dynamic, cardioid microphone close to the sound source. Interestingly, most
manufacturers are not interested in eliminating proximity effect as the extra low-
frequency boost is often desired by stage performers and newscasters (Huber & Williams,
1998). Figure 14 shows a spectrum of the vowel /a/ in “job” recorded with the AKG C-
420 microphone placed approximately 6 cm from the talker’s lips. This sample shows a
significant increase in amplitude in low frequencies around 100 Hz due to proximity
effect. While the measurements of F1 and F2 in this particular case might not be
significantly afi‘ected by proximity effect, the measurements of R) and other parameters

measured in the vicinity of the first 2 or 3 harmonics will be considerably biased.

Low-end bias due to proximity effect

 

l

95.74 % ,
‘/ \ F1 H

35.74

 

Sound pressure level (dB / Hz)

 

 

 

0 666:667 1333.33 2000
Frequency (Hz)

Figure 14. Low-end bias due to proximity effect of the AKG C-420 microphone

There are very few, specially designed microphones that retain broad and flat frequency

response from 20 to 20,000 Hz. Such microphones, particularly when coupled with 24-bit

33

digital recorders, are capable of capturing high quality, unbiased speech Signals. Figure
15 compares frequency response of Shure Beta 87a5 and Earthworks M30 microphones,
according to the manufacturers. The frequency response of the Shure Beta 87a
microphone has two significant peaks — one around 5,000 Hz (solid line), and one around
100 Hz (dashed line). The increase in low frequency amplitudes (dashed line) occurs
when the microphone is placed close to the sound source (below 6 cm). Conversely, the
low-frequency amplitude decreases when the microphone is moved away from the sound
source (above 60 cm). The Earthworks M30 microphone, on the other hand, retains a
relatively flat frequency response throughout the entire human hearing range (dotted
line), regardless of its distance to the sound source — subject to the limitations of the
hardware used in the remainder of the recording circuit. Section 2.5.3.3 contains a
detailed discussion on how proximity effect influences the analysis Of low-frequency

harmonics.

 

5 Shure SM48, a dynamic microphone similar to Shure Beta 87a, was once distributed with the Kay
Elemetrics Computerized Speech Lab (KayElemetrics, I998).

34

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-7 T ~4— T—p Microphone type
__+ i _ fr: Shure Beta 87a
é +10 . - g. I. --------- Earthworks M30
6 cm v ' ‘ ~ ‘ !
V ;’ s V. L] M
g ’ ‘ ~ ,. 1 R ..........
g o .. ------- --------------- r ““‘T‘T' e
'8. i, I
E —10 , 60 cm 1, LL Li \
l . 1' l 1__ ' 1
, —T"' . T . . _- I , -
L.___1__. L___L . i 11111 I i
2 3456789 2 3456789 °
20 50 100 1000 10000 20000 40000
Frequency (Hz)

Figure 15. Frequency response of Shure Beta 87a and Earthworks M30

2.3.6 Different signal acquisition methods return different formant values

Given the information in the previous section, one might suspect that LPC analysis would
output significantly different formant values across the three field recording methods. An
experiment was designed to test this hypothesis. A female talker with an NCCS-

influenced vowel system was recorded reading a wordlist containing words with four

American English vowels (/a/, /a3/, /e/, and /a1/). The first three vowels are participants in

NCCS, and the forth one, /aI/, was selected because of its diphthongal quality. The talker

was recorded with a Marantz recorder, an Audio-technica AT803b lavalier microphone
connected to a Sony MiniDisc recorder, and a Sennheiser head-set microphone plugged

into a Sound Devices USB Pre unit simultaneously.

35

A total of 99 vowel tokens were recorded and transferred to a computer workstation. The
first two sets were transferred via the digital S/PDIF6 interface, and the analog recording
was digitized at 24-bit and 48,000 Hz. The recordings were then downsampled to 16,000
Hz and analyzed acoustically. Acoustic analysis was performed by means of Akustyk

(Plichta, 2004).

2.3.6.1 How to avoid researcher bias in formant analysis?

It is difficult to run a test comparing three different sets of recordings without researcher
bias. This bias is caused by the subjectivity in identifying the steady state, an area of
relative stability in the frequency domain, where formants are to be measured (see, for
example Hillenbrand, Getty et a1. (1995) for a more detailed discussion on steady state
identification). Therefore, instead of identifying steady state and measuring formants
statically, a dynamic approach was designed. The vowel nucleus was divided into 0.0255
Gaussian analysis widows. Formants and bandwidths were measured and recorded for
each such window. Based on these data, the software calculated mean formant and
bandwidth values for the entire duration of the vowel, as well as their average cumulative
variation based on the algorithm given below, where v is the average variation over time,

x” is the frequency at point n and d is vowel duration:

 

6 S/PDIF (Sony/Philips Digital Interface) is a widely used digital audio interface capable of carrying stereo
data quantized at 24-bit and 96,000 Hz.

36

"2:7(xnfl —xn) (1)

d

 

v:

2.3.6.2 Statistical analysis of formants, bandwidths, and cumulative variation

Multivariate analysis of variance (MANOVA) was performed to test the hypothesis that
the three recording modes return different formant values. The dependent variables
included formant values in Hertz (F 1 through F3), formant bandwidths in Hertz (B1
through B3), as well as total cumulative variation the first three formants in Hertz/s (F 1v
through F3v), while the three different recording modes constituted the independent
variables. MANOVA was significant for recording type, F(18,188)=8.318, p<.001,
Wilks’ A=0.312. Because the overall MANOVA was significant, a series of post-hoe
tests was performed to discover which acoustic parameters were Significantly affected by
recording type, and it was found that recording type was Significant for all of F 1
parameters. The post hoc Bonferroni multiple comparisons test (at the significance level
of p<0.05) showed that all F 1 -related parameters were Significantly different from one
another across the three different recordings types. In addition, the tests found that all
vowels individually, exhibited a similar kind of variability. Table 1 summarizes the
results, where F1 is the mean first formant frequency, BI is the mean first formant

bandwidth, and F IV is the mean cumulative F1 variation.

37

 

 

Tests of between-subjects effects

 

 

F1 Bl Flv

Overall F(2,102)=17.741, F(2,102)=17.741, F(2,102)=3.781,
p<.001 p<.001 p<.025

Ig/ F(2,21)=1.882, F(2,21)=8.042, F(2,21)=.900,
p=.177 (ns) p<.001 p=.421 (ns)

/a/ F(2,21)=7.94, F(2,21)=4.544, F(2,21)=1.076,
p<.025 p<.057 p=.359 (ns)

/a,/ F(2,36)=6.12, F(2,36)=3.773, F(2,36)=4.15,
p<.025 p<.05 p<;025

m/ F(2,15)=8.737, F(2,15)=31.106, F(2,15)=2.972,
p<.025 p<.001 p=0.82 (ns)

 

Table 1. Summary of statistical results of the data acquisition test

The box plot in Figure 16 is based on the SO-called “quartiles,” which divide the
distribution into four equally filled intervals. The upper and lower edges on the boxes are
located at the first and third quartiles of the data, respectively (Jacoby, 1998). The
vertical lines above and below the boxes (so-called “whiskers”) extend to the upper and
lower adjacent values, or values that correspond to the maximum and minimum values in
the data set. It can be seen in Figure 16, for example, that the mean F1 computed from
the recordings obtained with the lavalier microphone has the shortest whiskers, indicating
a relatively small distance between the highest and lowest F1 fi'equency in the data set.
The horizontal line inside each box is the median line. If the line is Off-center, the plot

indicates an asymmetrical density of data points. Figure 16, for instance, shows that the

 

7 With this type of between-subject follow-up test, it might be argued that the ANOVA has to be significant
at the .025 level.

38

distribution of F1 values obtained with the head-set microphone is the most symmetrical,
while the F 1 distribution obtained with the built-in micrOphone is spread over the
broadest range of values. At the same time, none of the methods show any values beyond

the adjacent values, which indicates that no “unusual data” or outliers have been found.

 

1800

1600«

1400.

 

 

 

1200~-

 

 

 

1000‘

 

 

 

 

 

 

l31(HZ)

 

.1
Min-1

800“

 

 

 

 

 

 

600*

 

 

400*

200*

 

 

0

 

f T

Lavalier Bui lt-in Head-set
Microphone type

Figure 16. Box plot of overall between-subject comparisons of the data acquisition test

2.3.6.3 Summary of the signal acquisition experiment

As noticed in 2.3.6.2, the choice of data acquisition technology and methodology is an
important consideration. Depending on the signal acquisition method used, significantly
different results may be obtained across the F1 domain. Researchers Should be
encouraged to follow best practices in the area of speech recording, processing, and
analysis, and should use modern technology to their advantage (Plichta, 2002). There

should also be no doubt that signals acquired with the head-set microphone and a 24-bit

39

digital recorder are the most accurate, and, therefore, the most reliable. Such signals

contain the greatest amount of spectral detail and the least amount of unwanted noise.

The intent of this dissertation is not to advocate the abandonment of traditional methods,
nor is it to claim that all traditional sociophonetic research is unreliable. On the contrary,
there is an extremely rich sociophonetic tradition of acoustic analysis that has furthered
our understanding of language variation and change probably more than any other branch
of modern sociolinguistics. Still, NCCS-influenced vowels, due to their complex spectral

features, are difficult to analyze, and caution must be exercised in this type of analysis.

To illustrate this point. Figure 17 shows three LPC spectra of the vowel /a/ in the word

“lot,” obtained from the same corpus. LPC was applied at the same point in time and with

the same parameters (analysis width of 0.0255, the sample rate of 16,000 Hz, LPC filter

order of 13, and a mid-range pre-emphasis filter starting at 50 Hz). The vowel /a/, one of

the major participants in NCCS, is problematic for an LPC algorithm because F l and F2

are quite close to each other in frequency.

In poor quality, highly attenuated recordings (such as that in Figure 12), the distinction
between the two peaks is blurred and LPC can return an incorrect reading. Figure 17
shows three LPC filter response graphs superimposed on top on one another. The box
below the LPC graph contains the windowed (0.025s) portion of the waveform. The
dashed line represents the sample obtained with a built-in microphone, and, as can be
seen, it captures only one peak in the vicinity of F1. The same is true of the sample

recorded with the lavalier microphone (dotted line). It is only the recording obtained with

40

the head-set microphone that allows the LPC filter to return two formant values and a
very realistic-looking spectral envelope. There are many examples of this kind, and the

data acquisition test carried out earlier in this chapter supports this point as well.

 

 

 

 

 

 

 

 

 

 

 

F 1
F2
/
.\ \ I) /
‘~,\ ““\ F3
a ‘~.\ .4
g 604 /\
T)
>
2
G)
I—
:3
52
e 40 .
o. Microphone type
-o . .
c: —- —- Built-In
5
c2 ------ Lavalier
Head-set
20
0 5000
Frequency (Hz)
0.175243 0.187743 0.212743 0.225243
Time (s)

Figure 17. LPC spectra of the /a/ vowel in "job" by a female talker with an NCCS-
influenced vowel system acquired with by different methods

2.3.7 LPC analysis of nasalized vowels: Is Iael really raising?

Traditional acoustic analysis methodologies may have lead researchers to believe that the

vowel /2e/ in NCCS-influenced speech is significantly raised, while, it can be argued that

41

the reported, extremely low F 1 values, normally associated with increased vowel height,
are a result of a combination of instrumentation limitations and the application of LPC

(Linear Predictive Coding) acoustical analysis to nasalized spectra.

Numerous studies have reported F1 values of /a/ to be as low as 350-400 Hz (especially

in female talkers), particularly when followed by a nasal consonant (e.g., Labov et a1,

(1972) or Labov et a1, (1997)). Figure 18 shows two vowel systems obtained from female

Detroiters (Labov et al., 1997). Each Of these systems Shows significantly raised /z£/,

whose F1 is considerably lower in frequency than that of /e/. Most typically, such results

have been obtained from tape-recorded (analog cassette or MiniDisc with an omni-
directional condenser microphone) or telephone-recorded wordlist and conversational

data. Linear Predictive Coding (LPC) has most often been employed to extract formant

values (e.g., Labov (2001)).

 

Figure 18. Sample vowel systems of Detroit, MI females (from Labov et a1. (1997) with
permission)

LPC models the vocal tract as an all-pole filter and does not account for spectral zeroes

(anti-formants), which are present in nasalized speech sounds. In the presence of nasal

42

formants, even a small incorrect adjustment of the LPC filter order (a coefficient based
on the number of expected formants in a specific digital audio file) might easily return

incorrect formants values (e.g., a low F1 of /ee/).

While it is not always possible to review and re—analyze audio data used in previous
research, one might speculate that the application of LPC to nasalized vowels (e.g., /a:/ in
the word “man”), particularly with poorly recorded data, might have misrepresented the
picture of the /2e/ vowel’s movement within the vowel space, at least to some extent.8

Further in this chapter, it will become clear why this is might be the case.

2.4 VOWEL NASALIZA non

2.4.1 The velopharyngeal port

Vowel nasalization can be generally of two kinds: (1) contrastive (phonemic) and (2)
non-contrastive (allophonic or coarticulatory). In each case nasalization is caused by the

opening of the velopharyngeal port as shown in Figure 19.

 

8 Note that the vowel /E/ is susceptible to the same problems, except that it has been reported to be
lowering, not raising.

43

Lowered velum

Figure 19. Schematic view of lowering (opening) of the velum during the production of
nasalized vowels

The total area of the opening of the velopharyngeal port varies greatly among languages
and dialects, thus causing different degrees of vowel nasalization. The opening of the
velopharyngeal port creates an air passage between the oral and nasal cavities. This
cavity coupling changes the spectrum of speech sounds produced in this way. Figure 20
shows a schematic, and simplified, view of the physiology involved during the
production of nasalized vowels. In normal speech, the velum does not completely close
the pharyngeal passage to the nose, and the degree of opening is subject to coarticulatory

variability.

44

Nasal passages

12.5 cm

   

Velopharyngeal
opening

 

01 vi
raca ty\ 2cm .4——0.8cm2

 

4.0 cm?-

 

 

 

‘ 17.5 cm )

Figure 20. Schematic View of the oral and nasal passages (Of a male talker) involved
during the production of nasalized vowels (based on Chen (1997))

2.4.2 Oral and nasal formants

Just as the oral cavity produces its own resonant frequencies, so does the nasal cavity. For

instance, the spectrum of the nasalized vowel /zfi/ produced with oral and nasal

resonances reveals the presence of two extra peaks (nasal formants): one nasal formant is
typically between the first two oral formants (F2.,) and one at much lower frequencies,
below the first formant (F1,.).9 At the same time, the bandwidth of F1 is significantly
broadened (to around 200-300 Hz) and there is evidence of two additional zeroes in the
spectrum. Nasal formants and zeroes (or anti-forrnants) typically occur together as pole-

zero pairs (e.g., Chen (1997)). The relative frequency and amplitude of the nasal formants

 

9 Note that F2n is sometimes referred to as “E,” in the literature.

45

depend on the area of the opening of the velopharyngeal port. Figure 21 contains a
theoretical model of this phenomenon. Note that the first formant of the nasalized vowel
(F 1 n) is lower in frequency than the first oral formant (F 1) and that the extent of this
increases with the increase in velopharyngeal opening. Fn is the higher frequency nasal
formant (in this dissertation, referred to as F2“) and fl is a nasal zero (anti-formant).
Again, note how all of the above parameters change as the Opening of the velopharyngeal

port increases (also see 2.4.3).

 

1500

1000

 

 

Frequency (Hz)

500

 

 

l 1 1
0 0.2 0.4 0.6 0.8
Area of velopharyngeal opening (cmz)

 

Figure 21 A theoretical model of the relationship between the area of the opening of the
velopharyngeal port and the frequency and amplitude of oral and nasal formant for the

vowel /a=:/ (from Stevens (1998))

2.4.3 Spectral characteristics of synthetic nasalized vowels

In order to illustrate nasalized spectra in more depth, a series of four variants of the word
“sack” has been synthesized with increasing degrees of opening of the velopharyngeal
port according to the model proposed by Stevens (Figure 21). In Figure 22, the
velopharyngeal port opening is 0 m2, (left), and 7 m2, (right). In Figure 23 it is 14

mm2 (left) and 21 mm2 (right). As the opening of the velopharyngeal port increases, so

46

does the amplitude of the nasal formant F In. The spectral envelope acquires a different

shape with a great deal of energy close to the first harmonic and a significantly broadened

bandwidth and reduced amplitude of the oral F 1.

Sound pressure level (dB / Hz)

 

110

 

 

 

 

 

 

 

 

‘ F1
F2
90.4.
701-» -
0mm2
5 4 i
0 666.667 1333.33 2000
Frequency ( Hz)

Sound pressure level (dB / Hz)

110

 

 

 

 

 

 

Fl . F1
‘1 i F2
90....
70V .. .. l" ..
7 mm2
50 4 ;
0 666.667 1333.33 2000
Frequency (Hz)

Figure 22. Spectrum of the lm/ vowel synthesized with the velopharyngeal port opening
Of 0 m2, (left), and 7 m2, (right)

Sound pressure level (dB / Hz)

11

 

90-

 

 

 

 

 

 

 

70‘ d "
14 mm2
50 4 l
0 666.667 1333.33 2000
Frequency (Hz)

Sound pressure level (dB / Hz)

 

 

 

 

 

110
Fln F1 F2
i. ll
91%. III
I II
I all!
i I
”If ' I '3’
1' i : 21 mm2
50 i
0 666.667 1333.33 2000
Frequency (Hz)

Figure 23 Spectrum of the /a:/ vowel synthesized with the velopharyngeal port opening of
14 mm2 (left) and 21 mm2 (right)

2.4.4 Spectral characteristics of the oral ~ nasal contrast in Polish

AS mentioned in section 2.4.1, vowel nasalization is used contrastively in some

languages. Polish is one such language, and it has two nasal vowels: /E/ and /5/. Figure 24

shows a spectrogram of the minimal pair /kretG/~/krEtG/. The spectrogram of /kreta/

shows well-defined, high-amplitude formants with narrow bandwidths (the dark bands in

the illustration). while the spectrogram of /kr§ta/ shows a great deal of energy diffusion

around F l , as well as the presence of the nasal formant F l ...

 

Frequency (Hz)

 

 

 

 

lklrl

 

 

1 .37746
High energy. Time (5) Diffused energy,

naITOW bandWidth F1 wide bandwidth of F1 ,

a presence of F In

Figure 24. Spectrogram of a Polish minimal pair /kretc1/~/kréta/

Figure 25 shows a spectral “Slice” taken at the mid-point of each vowel nucleus. There is
no evidence of velopharyngeal opening in the non-nasal example. F1 has a well-defined
peak, and the spectral envelope shows an approximately -6 dB/Octave sloping pattern
characteristic of this mid-front vowel. The F1 peak is strong and has a narrow bandwidth.
The spectrum of the nasal vowel /E/. on the other hand, looks quite different. There is a
high energy concentration around the second harmonic — evidence of the low-frequency
nasal peak. Also, the F l is lower in amplitude and its bandwidth is significantly wider

than for the non-nasal vowel /e/.

48

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

/kréta/ c fleeta/
A 95 F1 L ‘ 7; 95 ‘ ‘
E [F1 ~ I \F1 F2 8
\ \ ,
'3' *’ . (1‘7 8 .3 /
T) 75‘ T) 751 > - '
E2 E.»
U 0
§ 55i‘ _ :g 55., 5 .. . . ._ . ........l.
D. O. I ‘
1: 1: 1 3 . ,
5 g " III Ill
0 O ,
m 35 . . ‘ m 35 . 1 .
0 1666.67 3333.33 5000 0 1666.67 3333.33 5000
Frequency (Hz) Frequency (Hz)
27.8132 27.8377 27.8867 27.9112 27.1314 27.1559 27.2049 27.2294
Time (s) Time (s)

Figure 25. Spectral characteristics of the oral ~ nasal contrast in Polish

2.4.5 LPC and nasalized vowels - evidence from Polish

Linear Predictive Coding (LPC) can be thought of as an all-pole digital filter with a
response similar to that of human speech. LPC has been used with a great deal success in
various computational areas — from speech recognition to digital watermarking (e.g.,
Gurijala, Deller, & Seadle (2002)). LPC has also been used in sociophonetics and has
constituted the predominant technique for extracting formant values from field-recorded

speech (e.g., Labov (2001)).

However, LPC does not work well with nasalized vowels. LPC algorithms cannot
account for the additional nasal formants and zeroes. This, coupled with poorly acquired,
highly attenuated, noisy recordings can result in the return of incorrect formant values

from LPC analysis. Figure 26 shows LPC and FFT spectra of the Polish minimal pair

/p'éta/~/peta/. In the nasal case (left panel), close to the LPC-retumed peak labeled “F 1”,

49

a strong nasal peak around the second harmonic can be seen. This peak is followed by
three harmonics that are very close to one another in amplitude. At the same time, the
second nasal peak can be found approximately half-way between the LPC peaks labeled
“F 1 ” and “F2”, and an additional anti-formant is predicted to be found in the area just
above 1,000 Hz (see Figure 21 for comparison). The LPC algorithm is not designed to
handle vowels with such complex spectra and it returns F1, F2, and F3 values that seem

to be averaged over the oral and nasal peaks and zeroes. By contrast, the spectrum of the

oral vowel /8/ (right panel) is not as problematic for the LPC algorithm, which returns

realistic approximations of formant values.

91.97

 

 

I

67.8" ' 7l.°7‘ E l:

 

47.81- 5 l .974

 

 

 

Sound pressure level (dB/Hz)

 

 

 

 

 

 

 

 

 

27.811 31.97 .
0

1666.67 3333.33 5000 0 1666.67 3333.33 5000

 

 

Frequency (Hz)

Figure 26. Comparison of LPC and FFT spectra Of /e/ and /§/

50

2.5 VOWEL NASALIZA TION IN MICHIGAN

2.5.1 Nasal formants appear in the spectrum of Lower Michigan vowels

Careful acoustic analysis of certain NCCS-influenced vowels, particularly /ae/, in non-

nasal environmentsl0 reveals evidence of two nasal formants. In the case of /ae/, for

example, one nasal formant, F2", is usually found between the first two oral formants (F l
and F2) and one, F1", is found below the first formant. At the same time, the bandwidth

of F1 is significantly increased, relative to vowels showing no signs of nasalization. This

makes the vowel /a=:/, for example, quite similar to both the theoretical model of a

nasalized vowel, as well as the real-life Polish example cited in 2.4.5. Figure 27 shows

two spectra of the vowel /a:/ in “back” - one without any evidence of velopharyngeal port

opening (left), and one showing evidence of at least the first nasal formant, F1n (right).

The non-nasal /a:/ was obtained from a young African-American female speaker from '

Saint Paul, MN (a Speech community not influenced by NCCS), while the nasalized
sample was obtained from a young female talker from the Detroit area in Michigan (an
area influenced by NCCS). The waterfall spectrograms in Figure 28 show the entire word
“back” and demonstrate the presence of nasal energy in more detail than the spectral

slices in Figure 27. Note high energy concentration and diffusion along the bottom of the

spectrogram of /bzék/ in Figure 28 (right), similar to that found in Figure 24.

 

0 . .
' Non-nasal envrronments are defined here as the presence of oral consonants or vowels both precedlng
and following the vowel. For example, “hat.”

51

m /,F1 . /baek/ FIR . F1 . /ba§k/

 

 

 

 

 

 

 

 

93
A 7.1 A’ A
53 F2 5: T / F2 -
\ / /a:/ \ _/ /2e/
a: co
3 3
T) 73" T) 73-
> >
.2 2
D
3 53- 3 53-
'5. ‘5.
a a
:l :l
o o
‘0 33 '. . ”1 33 . .
0 1666.67 3333.33 5000 0 1666.67 3333.33 5000
Frequency (Hz) Frequency (Hz)

Figure 27 Examples of non-nasalized vowel Spectra (left) and nasalized spectra (right) of
the vowel /a=:/ in "back"

/b2ek/ /baék/

 

Figure 28. Waterfall plots of the word "back" of the same sample as in Figure 24

2.5.2 Why a sociophonetic aerodynamic analysis of vowel nasalization?

In light of the evidence of potential data acquisition problems and their impact on derived
formant values, particularly F1, and because evidence of nasalized vowels has been
found during routine sociophonetic analysis of NCCS-influenced speech, it was thought

important to pursue an alternative diagnostic methodology to see whether nasalization is

52

regularly associated with NCCS vowels and, if so, how it patterns with sociolinguistic

factors such as sex, and region.

2.5.3 Quantifying vowel nasalization for sociophonetic purposes

Sociophonetic research within the variationist framework demands that variable phonetic
details be quantified either with objectively measurable values or researcher-subjective
indices (Chambers, 1995). Having established the presence of nasal cavity coupling in
non-nasal environments in certain Detroit-area individuals. one must now devise an
appropriate quantificational methodology to capture the social distribution of this feature.
There exist two methods of measuring nasalization; both have been designed primarily
for the purposes of diagnosing and correcting certain communication disorders, such as
nasalization due to cleft palate (velopharyngeal inadequacy). The first method,
advocated, among others, by Chen (1995), involves quantifying nasalization from
narrow-band spectra; the other involves an aerodynamic measurement with a specially
designed device, such as the Kay Elemetrics Nasometer II (KayElemetrics, 2003).
Nasometer is a head mounted device that registers oral and nasal airflows and reports

their ratio.

2.5.3.1 Quantifying nasalization from narrow-band spectra

Quantifying nasalization from narrow-band spectra has been proposed by several
researchers but Chen (1995, 1997) presented the most compelling and comprehensive
case. The method proposed by Chen is based on calculating amplitude ratios between oral
and nasal peaks. In this framework, the amplitude of the nasal formant, which is most

typically found between the first two oral formants, is called “P1” while the amplitude of

53

the nasal formant, typically found below the first formant, is called “P0.” Based on the
theory of speech production (see, for instance, 2.4), Chen assumes that the amplitude of
the oral F1 (called “A1”) will be substantially reduced due to nasal cavity coupling. For
Chen, this reduction in A 1 is essential to quantifying nasalization. Chen proposes

calculating two variables (Figure 29):

1. Al-Pl -— the difference in amplitude (in dB) between the first oral formant (A l) and
the second nasal formant (P1), and

2. A l-PO — the difference in amplitude between the first oral formant (A1) and the first
nasal formant (P0)

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F1n(P0)
91.69 / .4
i F1(A1) /é/in"ten"
,2 ,. ., .. anlPl) Al-P0=-9.66
a ’7‘; i / . F2 Al-P1=4.83
l
T;
5
e
5 51.6
g l
8
31.69 . .
0 1666.67 3333.33 5000
Frequency (Hz)
42.521 42.5455 42.5945 42.619
Time(s)

Figure 29. Oral and nasal prominences involved in Chen's method

54

Chen makes a strong case in favor of this approach and, from the theoretical standpoint,
this approach shows great promise. However, problems are likely to occur for the

following reasons:

1. It is not always obvious where the specific peaks are to be found. Vowels that have
only a small amount of nasalization or none at all will be particularly problematic for

this method.

2. This method is relative to the absolute formant frequencies of the vowel under
investigation and it requires an extra vowel-type adjustment procedure that is
necessary for finding the correct P1 and P0 peaks. Moreover, formant transitions due
to the phonetic environment preceding and following the vowel would require
adjustment as well.

3. This method only works when data are acquired by methods similar to those
described by Chen — with a high-quality omni-directional microphone (presumably to
avoid proximity effect) close to the talker’s lips and in a sound-proof room. One
might speculate that the output of this type of analysis would be strongly affected by
the type of recording equipment used and the circumstances in which the recording
was made. For example, most studio or stage directional microphones have
purposefully emphasized low frequencies and have a high degree of the proximity
effect, which is desired by stage performers and newscasters, but which can
compromise the reliability of the Chen method (see 2.3.5 for a discussion on role of
the microphone).

4. This method is not Optimal for variationist work, as one has to assume a priori, where
the nasal peaks are to be found (as a function of the velopharyngeal port opening, for
example).

It is largely because of these reasons that this method was not employed in the present

study as the main diagnostic tool. Instead, it was used as a secondary method.

2.5.3.2 Designing peak-seeking software for Chen’s method

Chen recommended looking for the peaks in the vicinity of the harmonics closest to the
peaks predicted from theory. Chen does elaborate on the theoretical model herself, but a

more detailed account can be found in Stevens (1998). The software would have to be

55

able to find the right peaks, verify them, get their frequency and amplitude values, and
compute all required variables (Figure 30). The most important, and most difficult, part
of the design process was to make sure that there was the least amount discrepancy

between the peaks picked by software and those picked manually.

 

 

 

Look for peaks at
112 vowel nucleus

1

Compute peaks and

Audio file at amplitildcs
11,025Hz/16-bit

Vowel type adjustment

Gepeat or S109

Figure 30. Flowchart ofAkustyk's algorithm designed to automate the peak seeking
process

 

 

 

 

 

 

30—ms Hamming window Discrete Fourier Transform

 

 

 

 

 

 

 

 

 

 

2.5.3.3 Successes and failures of the Chen method

AS applied here (see 2.8 for details), Chen’s method proved moderately successful. The
criticism about finding low frequency peaks turned out to be well-founded. In tests on
vowels recorded with different microphones, and on vowels recorded with low frequency
roll-off filters (very common on many preamplifiers and microphones), Al-PO was not
reliable, as the variable low-end part of the spectrum considerably biased the results (see
Figure 14). In such situations, one is faced with a difficult question of whether the
diagnostic method is flawed or whether it is the investigator’s responsibility to acquire

pristinely flat recordings. In this particular case, the method’s special sensitivity to low

56

frequency fluctuations made it very difficult to acquire reliable signals. In practice, only
the head-worn Sennheiser HMD 25-1 and the Earthworks M30BX measurement
microphone captured the speech signal without significant low-end bias. Shure SM58,
Audio-technica A83lb, and Audio-technica AT822 were also tested but proximity effect

compromised signal reliability.

On the other hand, the A l-Pl variable worked out relatively well, as the value of P1 was
much less likely to be affected by low frequency bias, and the difference between manual
and machine-obtained values was within a 10% tolerance. Perhaps the biggest problem

with Al-Pl was that for some female talkers, the difference in frequency between A1 and

PI peaks was very small, particularly for the vowel /a/. Thus, for instance, the F1 of /a/

will come very close in frequency and amplitude (e.g., neighboring harmonics) to the
predicted nasal peak at 950 Hz, and the difference in amplitude between these two peaks
will be very small. As a result, a high level of nasalization (i.e., small Al-Pl) will be
reported while the sample might not be nasalized at all (see a discussion on vowel type

adjustments in 2.8).

2.5.3.4 Quantifying nasalization from aerodynamic data

Quantifying nasalization with a Nasometer-type device is free from the problems of
Chen’s method. The method is based on a very Simple, yet robust design. Two
transducers are placed in two chambers separated from each other with a sound
attenuating barrier. Each transducer signal is fed into a separate channel of a stereo
(unbalanced) mini-plug connector. The microphones require a constant power supply

(also known as “phantom power”), so there must be a power source available — typically

57

a AA battery. The device connects to a stereo, line-level interface of a common 16-bit

sound card such as SoundBlaster 16.

Software captures the signal coming through the stereo channel, splitting it into the nasal
. and the oral Signals — left and right. It then performs a series of computations to Obtain
several common acoustic parameters, such as FO, F 1, and amplitude. The oral-to-nasal
energy ratio is then calculated and reported in real time. Partly because of the simplicity
of the design, and partly because of the availability of very sensitive transducers and
good sofiware to process the data, this method has the promise of becoming a good tool
for measuring vowel nasalization for variationist purposes. The only notable problem is
that the Standard nasometers, such as the Kay Elemetrics Nasometer II, are quite bulky,

heavy, and obtrusive.

While Nasometer Il may work quite well in a clinical setting, it is generally difficult to
use in the field. There is one type of nasometer, though, that has all the benefits of a
stationary one, but is small, portable, and unobtrusive. It is the Rothenberg mask
(Rothenberg, 1995). The Rothenberg mask (Figure 31) is made of a synthetic material
and is shaped to fit the human face, though it probably should not be used with persons

with a lot of facial hair.

58

 

 

 

 

 

Figure 31. The Rothenberg mask

The oral and nasal chambers are divided with a thick plastic barrier in order to minimize
sound leakage between them. There are also small, specially designed circular vents
placed in the mask to facilitate breathing and prevent muffling while wearing the mask.
The Rothenberg mask works almost exactly like other nasometers, but it has the added
benefit of calculating %Nasalance (also known as “F0 nasalance”) from the amplitudes

of only the fundamental frequency components of the nasal and oral acoustic energy.

["0 nasalance is less sensitive to specific vowel formants and voice pitch than is F1
nasalance, and is less sensitive to articulatory movements than are methods comparing
low-pass filtered airflows. The parameter that will be used in the subsequent analysis is
often referred to as %Nasalance, or %N. These two terms will be used interchangeably in
this dissertation. %N is calculated from the energy measured at the nares (An) and the

energy measured at the mouth: (A0).

59

All
A +A

0 II

100 (2)

 

%N=

2.5.3.5 Weaknesses of the airflow methodology

An obvious disadvantage is the need for the subject to wear a mask. However, thanks to
the circular Wire vents, and a very lightweight, small form factor design, the mask did not
cause any subjects in the present study significant problems. One must remember,
however, to disinfect the mask after each use. Another obvious limitation is the
possibility of sound leakage between the oral and nasal chambers. This problem is
generally not very serious. However, it does intensify when there is complete

velopharyngeal closure and a very strong oral constriction. This is why the non-nasal

high vowels /i/ and /u/ may appear to have higher %N than mid or low vowels. This is, of

course, contrary to the physiology of speech production. This problem occurs because the

high oral energy leaks into the nasal chamber and thus registers as high nasalance (also

see 2.6.4 for a detailed discussion).

2. 6 STUDY DESIGN AND METHODS
2.6.1 The goals
The present study investigated the degree of vowel nasalization across three previously
studied dialectal groups in Michigan: European American Detroiters (“LM”), European
American rural Mid-Michiganders (“MM”), and European American Upper Peninsula
talkers (“UP”). The dialects of Detroiters and Mid-Michiganders have been well

documented (Labov et al., (1972) and Ito, (2000) respectiveIY), as have other groups

participating in NCSS (Evans, 2001).

60

For example, it has been established that young, middle-class and upper-middle-class,
socially mobile individuals are most likely to exhibit advanced stages of NCCS. In
addition, women are often ahead of men in adopting these types of chain-like, vocalic
changes (Labov, 2001). The goal of this study was. therefore, to investigate the
populations identified in previous work as likely “vowel shifters” and to see whether
nasalization, or %N to be exact, correlates with their shifted phonology. It was not the
goal of this study to demonstrate a detailed social stratification of Michigan residents

with regard to vowel nasalization.

2.6.2 The subjects

The studies by Ito (2000) and Evans (2001) demonstrated that the adoption of NCCS in
Michigan is gradient with regard to region and sex. In order to study the nasalization
effect as a potential correlate of NCCS, it was essential to select subjects from
demographic groups that are likely to have vowel systems influenced by it. The sample
for this study was, therefore, drawn from among three distinct geographical areas —
South-Eastem Michigan (n=12), Mid-Michigan (n=8), and the Upper Peninsula (n=10).
The subjects were young (19-35) and of a middle-class or upper middle-class
background; all with relatively. loose social networks in ways described by Milroy
(1980). The subjects were screened for and free of the history of cleft palate, chronic
sinus problems, chronic respiratory problems, smoking, facial hair, and common speech

impediments.

61

2.6.3 Data collection

The subjects were asked to provide demographic information based on a sociolinguistic
questionnaire (Appendix B). The answers were entered into an electronic database. Next,
the subjects were recorded saying 50 monosyllabic target words, selected to offer a broad
range of vocalic and consonantal contexts (Appendix A)”. The recordings were gathered
with a Sennheiser HMD 25-1 microphone and a 24bit/48KHz digital hard disk recorder
to obtain the highest quality digital representation of the acoustic signal, and to ascertain

a flat frequency response.

Finally, the subjects. were recorded saying the same 50 words by means of the
Rothenberg mask (1995). The subjects were instructed on how to hold the mask in order
to minimize sound (and airflow) leakage and attenuation. The words were uttered one at a
time, with a 10 second pause in between to minimize potential coarticulatory effects
across word boundaries. The signal was recorded at the sample rate of 22,050 Hz and at a
16-bit bit-depth. Since most nasal energy is concentrated in the lower bands of the
spectrum, the effective frequency response of 11,025 Hz at l6-bit was sufficient to
capture the signal with a high degree of accuracy. The samples were acquired via a stereo

channel, with one channel for the oral, and one for the nasal data stream. Figure 32 shows

spectra from the oral channel (left) and the nasal channel (right) of the vowel /a/ in the

word “lot” by a female Detroiter.

 

” Target words with the high vowels /i/ and /u/ were not included in the corpus (see 2.6.4)

62

/a/ - oral channel /a/ - nasal channel

 

 

 

 

 

 

 

 

Sound pressure level (dB/Hz)
5 $
=——-*'——
CW‘
‘7— I!
:3
“‘me
r:‘
I] 5
— ._fo
.23?
—=—_‘__
if
_ a?)
‘45:;
c;_ 5‘
<6
:
§ $
C..___ __ __ _
&;:—:‘—— _______
:_ __
2
‘W‘ZD
C-
P
:f,_..>
4
L2:
>-

 

 

 

 

Frequency (Hz)

 

 

%Mramw 19* lil'l'll' [illll'i‘l'il‘il'ii .|,ll,.ililimiW .22.“). .1“- iii. ,1,,1, Iji } iflih hit Ii» WWW

0.478086 0. 502586 0 551586 0.576086 0.483134 0 507634 0. 556634 0.581 134

 

 

 

 

 

 

Time (s)

Figure 32. Spectra from the oral and nasal channel of /a/ in “lot”

2.6.4 Why /i/ and Iul were excluded from the study

There are two reasons why the high vowels /i/ and /u/ were excluded from the corpus:

1. Relatively low nasalization levels in the high vowels /i/ and /u/ are predicted on the
grounds of experimental studies, such as M011 (1962) and Krakow and Huffman
(1993) who demonstrated on x-ray and MRI images, respectively, that the velum is
raised during the production of high vowels in general.

2. There is a reported energy leakage from the oral to the nasal chamber during the
production of high vowels when there is both a complete closure of the
velopharyngeal port, as well as a strong oral constriction anterior to the velum
(Rothenberg, 1999). One would have been faced with a paradoxical situation - the
vowels that are predicted to be the least nasalized would have registered a relatively
high %N.

In addition, the vowels /i/ and /u/ have not been reported to be active in NCCS, unlike

some other dialects of English (for instance, the Southern Vowel Shift) where /u/-fronting

63

is an active process (F ridland, 1998). Thus, the decision to leave them out of the analysis

should be of little consequence to the overall findings.

2.6.5 Data processing

2.6.5.1 Audio data

The original 24-bit audio recordings to be used in spectral analysis were downsampled to
11,025 Hz/l6-bit. They were saved in lossless PCM (Pulse Code Modulation) format.
This made the data suitable for detailed acoustic analysis. A thorough acoustic analysis
was performed by means of Akustyk (Plichta, 2004), written specifically to obtain a
comprehensive spectral analysis of vowels. Akustyk offers advanced analysis tools, such
as “on-the-fly” regression analysis of formant trajectories. It also keeps track of all
acoustic and demographic data in a standard, SQL-compliant (Standard Query Language)
format. This minimizes the risk of misplacing or mislabeling data before it reaches
analysis software, such as Systat. Akustyk also allows statistical analyses of formants,
such as paired or independent samples T-test, discriminant analysis, and Principle
Component Analysis (see 2.9.1.2 for more information on formant extraction with

Akustyk).

2.6.5.2 Nasalance data

Data acquired with the Rothenberg mask did not require any additional signal processing.
The data, already stored on a workstation hard drive, were analyzed by means of the
OroNasal Mask System software (Glottal, 2002) and the parameter “% Nasalance” (%N),
derived from the ratio of oral to nasal energy at H) (see 2.5.3.4 for a definition of %N)

was calculated and entered into a database.

64

2.6.5.3 Nasalization data from spectra

As mentioned in 2.5.3.2, the Chen method (measuring nasalization from spectra) was
automated with Akustyk. The software analyzed the entire corpus in batch mode. All

analysis data were written to a database in real time.

2.6.5.4 Database queries

After all the data had been collected, computed, and written to the database, a query was

generated to merge the demographic data with the acoustic data and the nasalance data.

2.6.5.5 Useable data

Due to the more difficult nature of acquiring data with the Rothenberg mask and writing
it to a computer hard drive in real-time, there were slightly fewer useable %N tokens

(1382) than spectral tokens (1418).

2.7 STATISTICAL ANALYSIS OF %N

The null hypothesis associated with this study was that nasalization (%N) would be
idiosyncratic and randomly distributed with respect to NCCS. However, if the qualitative
spectrographic analysis in 2.5.1 was correct, then there would be a substantial
relationship between NCCS and coarticulatory vowel nasalization. To test these

hypotheses, the following specific research questions were formulated:
1. Will Lower Michigan respondents have a different level of nasalance than Mid-

Michiganders and UP respondents?

2. Will women have a different level of nasalance than men?

65

3. Will the vowels involved in NCCS (/a/, /w/, or /e/) be specifically related to
nasalization?

2.7.1 %N - two-way analysis of variance
The present study involved one continuous dependent variable, %N, and two factors:
talker sex and region (on three levels: LM, MM, and UP). The first tests conducted were

the omnibus, or overall, tests of the main and interaction effects of region and sex.

The test of between-subjects effects showed that region, F (2,1377)=164.536, p<.001, sex,
F(l.,l377)=224.010, p<.001, and their interaction, F(2,l377)=11.388, p<.001, were all
significant. Figure 33 shows overall means of % Nasalance across region and sex.
Because the interaction effect was significant, simple main effects tests and interaction

comparisons were performed. Their results are described below.

 

 

;-2 u"
1“; 25 i'-
-' .. _,ii .'
i. 1.2511110".
20 .. ..... 1,1 ...........................................................................................................
d,- 134‘,
‘P4‘:‘V. V

 

Mean %N
11
.. .1
E
I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

J .' ,
“ 1, I] 13 I
102 ...... iiffv ....... I _ f
' :13“. E ii i": ”ii E
.11 ._ .- , , Talker sex
0 .. iii: “‘21 “1'31“.“ Eiziiititiii'i'lifii}it. B Male
LM MM UP
Talker region

Figure 33. % Nasalance means by respondent region and sex

66

2.7.2 Follow-up test #1 — simple main effects tests

Because of the significant interaction effect found in 2.7.1, a follow-up test was
conducted to answer two questions that have a great deal of importance in sociophonetic

research:

1. Are the %N means across respondent regions significantly different for men and
women separately?

2. Are the %N means within respondent regions significantly different for men and
women for each region separately?

Table 2 contains a summary of answers to questions 1 and 2 above based on a family of

main effects tests.

 

Simple effect tests for men and women across the three regions

 

Females across regions F (2,1377)=1 55.616, p<.001
Males across regions F (2,1377)=45.707, p<.001
Females vs. males within LM F(1,1377)=179.579,p<.001
Females vs. males within MM F(1,1377)=79.514, p<.001
Females vs. males within UP ‘ F (1 ,1377)=21 .792, p<.001

 

Table 2. Summary of simple effect tests for men and women across the three regions and
for men and women for each region separately

2.7.3 Follow-up test #2 — pairwise comparisons

Table 3 contains a summary of simple effects tests comparing different regions within
sex categories. All pairs were significant except for male respondents between MM and

UP.

67

Simple effect tests for men and women across each region separately

 

Females between LM and MM F(1,l377)=117.971, p<.001
Females between LM and UP F(1,1377)=292.486, p<.001
Females between MM and UP F(1,1377)=l7.22, p<.001
Males between LM and MM F(1,1377)=71 .407, p<.001
Males between LM and UP F (1 ,l377)=54.369, p<.001
Males between MM and UP F (1 ,l 377)=.155, p=.694 (ns)

 

Table 3. Summary of simple effect tests for men and women across each region
separately

2.7.4 Follow-up test #3 - interaction comparisons

In addition to simple main effect analyses, an interaction comparison was conducted.
This test consisted of three tetrad comparisons to see whether the differences in %N
means across the regions were the same or different for male and female respondents.

Table 4 summarizes the results.

 

Differences in %N means across the regions

 

LM vs. MM for females vs. LM vs. MM for males F (1 ,l 377)=1 .922, p=.166 (ns)
LM vs. UP for females vs. LM vs. UP for males F(1,1377)=22.52, p<.001
MM vs. UP for females vs. MM vs. UP for males F(1,1377)=9.1942,p<.025

 

Table 4. Summary of three tetrad comparisons to evaluate whether the differences in %N
means across the regions were the same or different for male and female respondents

2.7.5 Summary of the statistical analysis of % Nasalance

Based on the statistical tests so far, as well as the nature of the data, the main findings can

be summarized as follows (also see Error! Reference source not found.):

68

1. Women have significantly higher levels of nasalization than men across all regions.
2. Women have significantly higher levels of nasalization than men within each region.

3. Nasalization levels for women increase significantly in a gradient manner from the
Upper Peninsula, through Mid-Michigan, to Lower Michigan.

4. Nasalization levels for men do not increase in the same manner as those of women.
The Lower Michigan group is ahead of both Mid-Michigan and the Upper Peninsula,
but Mid-Michigan men and Upper Peninsula men do not differ from each other.

2.7.6 ls nasalization global or local?

One of the most important questions about nasalization in non-nasal contexts is whether
it is a global or a local phenomenon. In other words, does it affect specific vowels or is it
generalized over the entire vowel inventory? As mentioned in 2.6.4, the high vowels had
been left out of the analysis, but for the remaining vowels, %N turned out to be
statistically significant for the group as a whole, F(7,1127)=2.349, p<.05. Subsequent

pairwise comparison tests showed that the only significant difference was between the

vowels /a:/ and /e/ for male talkers.

The above findings indicate that vowel nasalization in non-nasal contexts is a
phenomenon generalized over the entire vowel inventory. Figure 34 shows mean %N
values for men and women for non-high vowels. Also, note very similar distributions

patterns of %N among male (gray bars) and female talkers (white bars).

69

 

18.0

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

160*
2 14.0
0
o\
x:
:3 4
o 12.0 ......
2 :i‘ 1
10.0‘ I“
i. 1 .I 1* 4"" * i [:l
80. " 4 ' = - . ‘ = Males
.1, 5......
60 3.::.:’-’;‘.'-:;-‘- a; i "47"." i
a E E I 3 O U A
Vowel

Figure 34. Male and female %N distribution patterns by vowel in non-nasal environments
and for non-high vowels

2.8 STATISTICAL ANALYSIS OF A1 -P1

As mentioned in 2.5.3.3, the Chen method proved to be relatively reliable for the
computation of AI-PI. For reasons described in 2.5.3.3, this method was used as a
secondary method, primarily to see whether measuring nasalization reliably from spectra
in batch mode was possible. A] -P1 was subject to the same type of statistical analysis as
%N, both because of the nature of the data, and also to be able to compare the two
methods more closely. The spectral method yielded very similar overall results to the
aerodynamic method. Table 5 summarizes overall means and standard deviations. Note a
rather high standard deviation for AI-PI obtained in the present study. Chen (1995)
proposed a vowel type adjustment procedure to alleviate some of the vowel-specific
problems listed in 2.5.3.1. The amplitude P1 in AI-PI is to be replaced by (Pl-T142) in
dB calculated according to the formulas below, where T1 is the effect of the first formant

on the extra peak due to nasal coupling, 72 is the effect of the second formant

70

component, F 1 is the first oral formant frequency, F2 is the second oral formant
frequency, B1 is the bandwidth of F l, and 32 is the bandwidth of P2. The constant value
of 950 Hz is derived from the theoretical model of the predicted frequency of second

nasal formant (see Figure 29).

 

 

n _ (0.5191)2 + F12 3

— [((0581)2 +(950— F1)2)((0.5B1)2 + (F1 +950)2)]112 ( )
(0.532)2 + F22

(4)

T2 =
“(O-532V + (F2—950)2)((o.532>2 +(F2+950)2)]”2

Despite the vowel adjustment procedures recommended by Chen, it was not possible to
obtain smaller standard deviations (see Table 5). Chen obtained smaller standard
deviations primarily because of her subjective peak picking process, as well as a very
uniform (and small) speech corpus. Machine-aided peak-finding, particularly over a

diverse corpus, is subject to more variability than a fully manual method.

 

 

Spectrum-derived parameter Minimum Maximum Mean Std. Deviation
AI—PI (dB) -24.09 50.64 13.7942 12.3059
A1 Frequency (Hz) 205.00 1265.00 677.3605 201.8381
P1 Frequency (Hz) 765.00 955.00 889.3300 61.5894
F2 Frequency (Hz) 667.00 3243.00 1697.9605 419.8848

 

Table 5. Overall means and standard deviations for the parameters obtained with the
Chen method

2.8.1 Summary of the spectral method

Because a detailed statistical summary of %N was included in 2.7, only a brief summary

of the spectral method will be included here (Figure 35; also, compare with Error!

71

Reference source not found.) Note that the exact same statistical procedures were

applied to both methods.

1. Region, F(2,l412)=13.448, p<.001, and talker sex, F(l, 1412)=158.530, p<.001,
were significant, but not their interaction, F (2, 1412)=2.526, p=.08.

2. LM men had significantly higher nasalization levels than both MM, F(l ,
1412)=7.740, p<.025 and UP, F(l, 1412)=19.497, p<.001, but LM and UP men were
not different from each other, F (1, 14l2)=3.162, p=.076.

3. LM women had significantly higher nasalization levels than both MM, F(l,
1412)=8.200, p<.025 and UP, F(l, 1412)=4.144, p<.05, but LM and UP men were
not different from each other, F (l , 1412)=.409, p=.523.l2

4. Within each region, men and women were significantly different from each other.

24

 

22‘
20«
18‘
I6

14.

12 - . a ..........
------ £3
. — ' ‘ Talker sex

Estimated Marginal Means A 1 -PI

10‘
[3'
8

- - - Female
Male

 

 

 

 

LM MM UP
Talker region

Figure 35. Summary of A 1 -PI results obtained in the spectral method (higher A I-PI =
lower nasalization)

 

'2 Note that this result is different than that obtained in the N% analysis, were MM and UP women had
significantly different nasalization levels.

72

2.9 NASALIZA TION AND NCCS

Having established major patterns of distribution of vowel nasalization in Michigan, it
was important to see whether nasalization was in any way related to the on-going vocalic
changes that are part of NCCS. As mentioned in 2.5.1, the first spectrographic evidence
of increased levels of nasalization in non-nasal environments occurred during standard
spectral analysis of vowels from Detroit-area female talkers. Subsequent aerodynamic
analysis confirmed high levels of nasalization in this population. Figure 36 shows

9

continuous levels of %N over the course of the words “dad” (dashed line) and “man’

(solid line). The levels of nasalization of /ae/ in a non-nasal environment (“dad”) are

almost as high as those of /2B/ in a nasal environment (“man”).

100.00 ,,

\l
5'"
o
O

50.00. -

 

Percent Nasalance

N
5"
o
‘3

0.00 __-.,_ _ ~_..-_ _.
30 100 130 200 250 300 350 400

Time (ms)

 

 

 

Figure 36. Continuous % Nasalance levels for the words "dad" and "man

73

2.9.1 The F2 of /a/ as an index of talker participation in NCCS

2.9.1.1 Which low-level acoustic feature is the most stable predictor of talker
participation in NCCS?

Finding a good candidate for the marker (or predictor) of talker participation in NCCS is
not easy. Ideally, one would need a feature that has the most stable production
distribution, as well as perceptional salience. Experimental sociophonetic data on

perceptual salience in NCCS is rather difficult to find. However, as will be shown in
CHAPTER 3, /a/ has been found to have a robust, generalized perceptual effect. It also
happens to be the least controversial from the point of view of both acoustical
measurement and sociolinguistic theory, as its movement in NCCS is practically only

along the dimension of F2. Therefore, the FZ of /a/ has been chosen as the best predictor

of talker participation in NCCS in the present study.

2.9.1.2 A comment on the acoustic analysis of /a/

It may seem inconsistent to perform acoustic analysis of /a/ in light of possible

nasalization issues (see 2.5.1). However, careful analysis of very high-quality speech data

with Akustyk makes such analysis of /a/ acceptable. A kustyk has a number of safeguards

against incorrect LPC-derived formant values, though these safeguards do not make the
software impervious to serious problems stemming from the analysis of highly
attenuated, noisy recordings. First of all, Akustyk dynamically estimates optimal LPC
filter order based on input sample rate and talker sex. It then performs LPC analysis
(either on a static analysis window or pitch-synchronously Figure 37) with the

automatically obtained LPC filter order, which it dynamically increments by 2 units

74

above and 2 below. If it detects possible LPC errors, it displays a warning and allows the
investigator to decide which filter order works most reliably before the data are written to
the database (see “safety algorithm” in Figure 37). In addition, Akustyk can be configured
to obtain formant frequencies from narrow-band spectra, and not from LPC. This is
particularly useful in the case of nasalized vowels. Akustyk first gets LPC-derived values,
subject to the investigator’s approval, and then searches for the highest-amplitude
harmonics within a user-defined number of FF T bins around LPC-obtained peaks
(typically, each bin is 10 or 20 Hz wide). This helps avoid incorrect formant values, such
as those obtained from LPC analysis alone (see 2.4.5). In addition to static analysis,
Akustyk performs interval analysis at 10 ms intervals along the entire duration of the
vowel nucleus, which allows the investigator to obtain a detailed account of the spectro-
temporal properties of formant trajectories. Akustyk employs a formant transition
algorithm (see Figure 37) to define formant transitions in acoustic terms. The investigator
can choose whether or not to include formant transitions in the interval analysis of

formant trajectories (via the “safety algorithm” in Figure 37).

75

Safety algorithm Interval analysis at I
/10 ms time steps

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5000 /
A 1741
N 2 Z I I
g E
5 F3
5 8 W
:2
E
éu~ _ .
...... a
0. I
t I / at d ;
0 0.576539
Time (s) :
Nasalization analysis Static LPC analysis Formant transition
from narrow-band spectra on a fixed analysis frame detection algorithm
Formants from /
narrow-band spectra or p1tch-synchronously

Figure 37. Overview of Akustyk 3 analysis tools

2.9.1.3 Which normalization scheme to choose?

Most modern sociophonetic research employs vowel formant normalization when
conducting quantificational analysis among talkers of different sex and/or age (Labov,
2001). Formant normalization is not to be confused with talker normalization (see
1.4.4.2), which is a perceptual phenomenon, though formant normalization aims at
achieving a similar effect. It is a computational transformation of raw formant values in
Hertz to account for the differences in vocal tract length, most typically, between men
and women. There are several normalization schemes with varying scatter reducing
power (Adank, 2003). Two of them, Nearey (1977) and Lobanov (1971), use a statistical

approach, computing normalization by means of log mean (Nearey) and z-score

76

(Lobanov) transformations. Nordstrom and Lindblom (1975), on the other hand,
normalize by multiplying female formant values by a coefficient derived mathematically
from F3 values of male and female talkers in the corpus. Each of these methods has its
own advantages - the Lobanov method has the best scatter reducing power. Nearey’s
formula has been widely used is sociophonetics (Labov, 2001), and Nordstrom and
Lindblom’s scheme takes into account actual anatomical differences. However, all these
normalization schemes are production-oriented. For the present study, it was important to

choose a normalization scheme that has real meaning in auditory processing.

2.9.1.4 Why Bark transformation works best

Vowel nasalization is a direct function of the velopharyngeal port opening. Therefore,
one might expect %N not to be related to general anatomical differences among male and
female talkers. While it is true that female nasal passages are slightly shorter than those
of men, the effect of the possible energy loss due to cavity size differences would
probably require a study beyond the scope of this work. For the purposes of this
dissertation, one might assume that a 1.5-3cm difference in nasal cavity size would be
rather insignificant. Besides, the aerodynamic measurement of %N is derived from the
ratio of oral to nasal energy, while the spectral method is based on the ratio of oral and
nasal formant amplitudes. Thus, both variables should be, by design, independent of

differences in vocal and nasal tract lengths between men and women.

Thus, one is faced with a situation where the dependent variable, %N, is measured on an

absolute scale, shared equally by all speakers, and only the F2 of /a/ needs to be

normalized for vocal tract length differences. Talkers with the velopharyngeal port open,

77

will deliver to their listeners’ auditory systems a signal whose spectrum contains nasal
peaks and zeroes. Somehow, the listeners will have to normalize that signal in order
process it correctly. Because vowel nasalization has such a complex bearer-mediated
component (see 4.2.2), it was necessary to use a normalization scheme that has a real
meaning in auditory processing, which is why the Bark scale transformation (e.g.,
Zwicker & Terhardt (1980)) was selected to be the most appropriate. Figure 38 shows a
cochleagram of the word “back” by a female Detroiter (the same token as that in Figure
27 and Figure 28). Cochleagrams, which combine the features of auditory spectra and
spectrograms, use shades of gray to illustrate spectral amplitudes with time, along the x-
axis, and frequency, along the y-axis. In the present example, the more common Hertz

scale has been replaced with the Bark scale to illustrate the complex auditory properties

of the nasalized vowel /é/ as it relates to the notion of “critical distance” (Chistovich,

1985). While “ordinary” spectrograms represent acoustic properties of sounds,
cochleagrams constitute their auditory representations. Figure 38 shows two prominent
frequencies (dark lines) below 5 Bark (approximately 500 Hz), which indicates their
potentially important role in auditory processing. These two peaks result from nasal

cavity coupling in ways described in 2.5.1.

78

 

  

 

 

 

25 -25
W .....
20 ...... -20
5f; 152 .. -15
8
8
a“: 111 -10
5‘ -5
.. , _ . --- /$/ in "back"
A r I I I 0
0 0.1 0.2 0.3 0.4 0.5

Time (s)

Figure 38. Cochleagram of lae/ in “back” by a female Detroiter

2.9.1.5 Bark-transformed Fl of /a/

An index of talker participation in NCCS was created, based on Bark-transformed second
formant (“fronting”) frequency in Hz. The raw F2 values were extracted from a word list
containing 10 instances per subject of /a/ across several different phonetic environments

(see Appendix A). F2 extraction and Bark transformation were done with Akustyk

(Plichta, 2004).

2.9.1.6 NCSS and phonetic environments

Chain shifts, including NCCS, are believed to be general in nature, and not specific to
any particular phonetic environment, though, of course, some environments may cause
formant values to vary quite considerably. This is primarily due to articulatory gestures

necessary for the preceding and following consonant, or lack thereof (Stevens & House,

79

1963). Although sometimes sociophoneticians talk about particular environments as
“promoting or demoting vowel shifts,” (e.g., Ito (2000) or Labov (1994)) this is not the

meaning in which NCCS is understood in this dissertation.

2.9.2 Investigating the correlation of %N and Bark-transformed F2

Linear regression was chosen to investigate the relationship between %N and F2 and to
test the hypothesis that the increase in %N might be predicted by the degree of fronting of
/a/, as revealed in higher F2 values (based on the corpus described in 2.6.2). The scatter
plot in Figure 39 shows the results for 30 subjects where %N is plotted as a function of
each subject’s F2 of /a/ (measured in Bark). The results indicate that the two variables are

linearly related such that as F2 increases, so does %N. The scatter plot of Bark-

transformed F2 and mean %N follows the regression line in a regular fashion.

80

 

40

30‘

Mean % Nasalance

 

 

 

 

T

9 9.5 10 10.5 1.1 11.5 12

Bark-transformed F2

Figure 39. Scatter plot of Bark-transformed F2 and Mean % N fitted around the
regression line

2.9.2.1 Regression results

The regression equation for predicting the overall %N scores was: Predicted %N = 6.39
Bark-transformed F2 — 50.811. The correlation between Bark-transformed FZ and %N
was .681, t(28)=4.9, p<.001. Bark-transformed F2 also explained a significant proportion

of variance in %N, R2 = .463.

2.9.3 Summary

2.9.3.1 Summary point #1

There is evidence to suggest that traditional signal acquisition and analysis methods may

have lead researchers to imprecise claims about some NCCS vowels, particularly about

the raising of /ae/ and the lowering of /e/.

81

2.9.3.2 Summary point # 2

There is evidence that some Michigan talkers pronounce vowels in non-nasal

environments with spectra containing components of nasal energy.

2.9.3.3 Summary point # 3

It has been established that %N is distributed in Michigan along the sociolinguistic
isoglosses of sex and region. Women have higher levels of %N both across and within

the three regions (LM, MM, and UP).

2.9.3.4 Summary point # 4

The %N distribution among men is different. LM men have higher levels of %N than
both MM and UP, but there is no significant difference between MM and UP males with

regard to %N.

2.9.3.5 Summary point # 5

%N is correlated with Bark-transformed F2 in the Michigan sample. Since F2 is a strong
predictor of NCCS participation, it is likely that %N is distributed along the lines similar

to those of NCCS.

2.9.3.6 Summary point # 6

The results obtained in the machine-aided spectral analysis of nasalization yield
additional support for the fact that region LM (most influenced by NCCS) is ahead of

both MM and UP in levels of vowel nasalization.

82

CHAPTER 3
PERCEPTIONS OF /a/-FRONTING

3. 1 INTRODUCTION
3.1.1 Introduction to talker normalization

3.1.1.1 What is talker normalization?

The speech signals that a listener must process are extremely variable (Ohala, 1981). The
sources of this variability are of several different kinds. One is between-talker variability
in vocal tract length (V TL). VTL varies primarily across talker sex (men vs. women) and
age (children vs. adults), and it directly affects formant frequencies of all vowels

produced by a talker (Stevens, 1998). For example, in Figure 40, the resonant frequencies

(formants) of the vowel /a:/, as in “back,” produced by males and females from the same

speech community may vary due to VTL differences by as much as 300 Hz along F1 and
F2. Three hundred Hz is sufficient to produce confusions among vowels in theory
(Peterson & Barney, 1952), yet listeners generally have little trouble with correct vowel
identification. They apparently make a perceptual “correction” for talker differences, a
phenomenon referred to as “talker normalization”(Nearey, 1989). Figure 40 shows an
F1/F2 plot of mean non-normalized male and female formant values from the Peterson
and Barney corpus (1952). Note that most of the male and female vowels are distributed
along very similar lines, i.e., they occupy similar relative positions in the vowel
quadrilateral. The female formant values are generally higher in frequency due to VTL
differences, as shorter vocal tracts produce higher frequency formants. Other common
types of variability for which listeners must also perceptually adjust include variation in
speaking rate and discourse frame (Lindblom, 1963). Vowels generally become reduced

(more schwa-like) with increase in speaking rate and decrease in syllable stress. Finally,

83

there is notable vowel variability due to differences in consonantal context (Stevens &

House, 1963).

 

   
 

 

 

 

20
[:1
A: ‘_ m
‘ U
V 2 A):
’13 i ' -
i ; . 3
5 600+ $ ._ ,. _, ...........
L‘- 1 (Fl—664, 1221127) E g
T 3 E1
in
A Females (Pl *—863. F2=2049) [A A
DMaIes ’ V
1000 1‘ 1 1
3150 2520 1890 1260 700

F2 (HZ)

Figure 40. Differences in F1 and F2 due to VTL variability between men and women
(from the Peterson and Barney corpus (1952))

3.1.1.2 Intrinsic normalization

Proponents of intrinsic normalization theory argue that there is information within the
spectral content of the vowel itself to support a listener’s normalization computation.
Two of the most convincing studies in intrinsic normalization are those by Miller (1989)
and Syrdal and Gopal (1986). Miller demonstrated that monophthongal vowels of
American English can be represented as clustered in perceptual target zones within a

three-dimensional (F‘3-F2, F2-Fl, and F 1 -SR'3) auditory perceptual space. Miller

 

'3 “SR” is “Sensory Reference” — a reference model proposed by Miller.

84

examined a number of speech corpora including the Peterson and Barney corpus (1952)
in this way. Syrdal and Gopal used a Bark transformation to devise a two-dimensional
model of vowel recognition based primarily on the perception of critical distance, in Bark
(Chistovich, 1985). The evidence for intrinsic normalization came from successful

discriminant analysis of vowels in thus derived vowel space.

A limitation of these studies is that they did not include sources of sociolinguistic
variability as it relates to vowels. Both studies used the Peterson and Barney corpus,
which contains a well-documented, but limited database of formant values from a select
set of monosyllabic words; all produced in /th/ context. Very little is known about the
speakers’ dialect history. The other corpora used by Miller are also void of documented

sociolinguistic variability.

To illustrate the potentially important role of sociolinguistic (dialectal) variation, a Bark
analysis, similar to that of Syrdal and Gopal (1986) was applied here to a corpus of
NCCS vowels and to the Peterson and Barney corpus. Figure 41 shows a two-
dimensional plot of the Peterson and Barney data delimited by F3-F2 and F 1 -F0 in the
discriminant plane. It can be seen that each vowel occupies its own unique (non-
overlapping) spot in the discriminant space (delimited by the ellipses), which suggests
that, with intrinsic normalization, there should, in principle, be very little vowel

confusion.

85

 

10

  

F3-F2 (Bark)
‘1"

 

 

 

0 2.5 5 7.5 10
Fl'FO (Bark)

Figure 41. Bark-transformed, discriminant plane of formant values from the Peterson and
Barney corpus '

Figure 42 shows a Bark-transformed discriminant plane of formant values from a corpus

of 26 adult NCCS speakers.I4 There is substantial overlap among /a:/, /e/, /o/, and /A/ in

this corpus, which suggests that confusion would be more likely to occur in communities
where NCCS is in progress. These results also suggest that in such communities the

success of intrinsic normalization strategies, such as that of Syrdal and Gopal, is less

likely.

" The Peterson and Bamey corpus is ofien praised by speech scientists and speech engineers for its
rigorous signal acquisition methods. This reputation is well deserved. However, modern technology,
particularly, 24-bit digital audio recorders and flat-response microphones. often make today’s signal
acquisition methods even more reliable than those of Peterson and Barney.

86

10

 

F3-F2 (Bark)
‘1"

   

 

 

 

0 2.5 5 7.5 10
171-170 (Bark)

Figure 42. Bark-transformed, discriminant plane of formant values from a corpus of 26
adult NCCS speakers

3.1.1.3 Extrinsic normalization

Proponents of extrinsic talker normalization argue that talker normalization requires a
perceptual frame of reference that is established based on information obtained from
sources beyond the vowel itself. It might, for example, come from information obtained
elsewhere in the utterance, or from extralinguistic sources, such as visual information
about the talker (e.g., Massaro, (1998)). The subsequent sections of this chapter will be
devoted to a version of extrinsic talker normalization that can be termed “sociophonetic

talker normalization.”

3.1.1.4 Sociophonetic talker normalization

Sociolinguists are interested in articulatory-acoustic variability due to socially-

constructed behavior. These low-level phonetic variants are essential to our

87

understanding of language variation, as they help us identify social forces behind

language change.

Sociophonetic talker normalization focuses on the integration of sociolinguistic (or
dialectal) features both in the top-down and bottom-up processing of speech (see 1.4.3).
Sociophoneticians are interested in testing the hypothesis that the variable acoustic signal
carries a wealth of sociolinguistic information, such as talker age, gender, socio-
economic status, educational background, and so forth. Over time, repeated exposure to
this information may result in the development of an abstract representation of idiolect
and dialect. Folk linguistic studies, such as those mentioned in CHAPTER 1, have

demonstrated this fact fairly convincingly.

3.1.1.5 Information conveyed by vowels

Ladefoged and Broadbent (1957) demonstrated that linguistic information conveyed by a
vowel does not depend on the absolute values of its formant frequencies, but on the
relationship between the formant frequencies of that vowel and the formant frequencies
of other vowels produced by that speaker. Being one of the first studies to use a speech
synthesizer, it provided a compelling account of talker normalization vis-a-vis talker
physiology. By shifting formant values of the entire vocalic system of a synthetic
precursor phrase by a constant value, Ladefoged and Broadbent obtained different

perceptions of specific F 1 /F2 vowel patterns that followed the precursors.

88

3.2 THE STUDY

3.2.1 Motivation for the study

Ladefoged and Broadbent (1957) pointed out three types of information conveyed by
vowels: linguistic information, sociolinguistic information, and personal information.
While their experimental study focused on the relationship between personal information
and vowel perception, they made two interesting observations following their

experiment:

1. “There is tentative evidence that subjects belonging to different sociolinguistic
groups gave different responses to some of the test material” (p. 103).

2. “It seems at least possible that both linguistic and sociolinguistic information
conveyed by vowels depends largely on the relative positions of their formants” (p.
103). '

Surprisingly, the provoking ideas of this 1957 study have not been given much attention
within the sociophonetic community. The present study was designed as a first attempt to
answer Ladefoged and Broadbent’s unanswered questions regarding sociolinguistics. The

specific research questions were formulated as follows:

1. Does talker-dependent, sociolinguistic information influence speech perception?
2. Do hearer-dependent, sociolinguistic factors influence speech perception?

3. What is the nature of talker normalization involved in this process?

4. Is /a/-fronting perceptually salient among NCCS speakers?

3.2.2 Lower Michigan and the Upper Peninsula

Lower Michigan (LM) and the Upper Peninsula (UP) (Figure 43) are two different

dialectal regions of Michigan. They differ in other ways as well. Ishpeming in the Upper

89

Peninsula, for example, is a small town in the vicinity of the Marquette Iron Range. It is a
working-class town and it is known for iron-mining activities, lumbering, marble
quarrying, and winter sports. The population of the Ishpeming-Marquette area is mostly
of Scandinavian origin. Detroit in Lower Michigan, on the other hand, is a large
metropolitan area. It is known for being a center of American automotive industry.
Detroit is a very dynamic, ethnically, and linguistically diverse city. The European
American middle-class and upper-middle class majority have been reported to speak a

dialect of English influenced by NCCS (Labov et al., 1997).

   
   

The Upper Peninsula
Ishpeming 0

Lower Michigan

Detroit 0

 

Figure 43. Lower Michigan and the Upper Peninsula

Linguistically, the dialect of English spoken in the Upper Peninsula is distinct from that
of Lower Michigan. It is under Canadian influence (Canadian raising) and it rarely shows
elements of NCCS. The standard F 1 /F2 vowel chart below (Figure 44) contains
normalized (Nordstrom & Lindblom, 1975) mean formant data extracted from recorded

speech samples from the UP and LM men (n=10) and women (n=9) participating in the

90

present study. Most of the differences are in the vowels /a/, /ae/, and /s/, which is not

surprising as those three vowels are active participants in NCCS. Table 6 contains

normalized mean F l and F2 values for the two groups obtained from the word list in

 

 

 

 

APPENDIX F.
20 3 ;
E} @
4004mMmmmehwwflflw v.2.wmdu,wmw.flgmmmwmm. ..m,.w_.nm
©
’1? z .
E 600‘ % ..
1: F
@D
800... i ..............................
[:1 LM respondents? :
O UP respondents
1000 + i 1
3150 2520 1890 1260 700
F2 (HZ)

Figure 44. Normalized, mean formant values of LM and UP participants in the study

91

 

LM respondents UP respondents

 

 

Vowel F1 F2 F1 F2
/i/ 264 2361 258 2432
/1/ 449 1826 429 1888
/8/ 658 1569 607 1822
/m/ 640 1796 728 1755
/a/ 803 1357 742 1258
/o/ 690 1 142 745 1223
/u/ 513 1207 513 1034
/u/ 351 1318 334 1182
/A/ 643 1310 610 820

 

Table 6. LM and UP respondents’ normalized, mean F l and F2 values in Hertz

3.2.3 Talker normalization across the LM and UP dialects

If listeners make a perceptual adjustment for dialectal differences in a talker’s production
of vowels, it might be expected that the same vowel sound input would be interpreted
differently depending on dialectal context. This perceptual effect might also depend on

the listener’s dialect history. These predictions were tested in the present study.

3.2.4 The subjects

The subjects were recruited from the Detroit area of Lower Michigan (5 men, 5 women,
ages 19-30) and from the Ishpeming area of the Upper Peninsula (5 men, 4 women, ages
19-34). All of the subjects of were of European American descent. The subjects had to

have been born and raised in their respective region and to have never left their region for

92

more than a year. They had to be native speakers of English. Figure 44 shows their
normalized, mean vowel systems within the F 1/F2 space. Similar to the study in
described in 2.6.2, the subjects were selected from among groups that would, in theory,

be likely participants in NCCS.

3.2.5 The stimuli

3.2.5.1 Speech synthesis

As mentioned in 1.4.4.3, synthetic speech has often been employed in speech perception
research. There exist several different types of speech synthesis, out of which three are
the most common: parametric speech synthesis, synthesis from voice samples, and LPC
analysis/re-synthesis. Parametric synthesis creates speech-like digital samples from
numerical input to common speech production parameters, such as oral constriction,
degree of breathiness, voicing, and others. This type of synthesis is the most difficult to
use in large-scale, commercial projects, such as automated phone services, but it is very
effective in small-scale empirical research as it allows precise control of the signal.
Speech synthesis from voice samples, or concatenative synthesis, (synthesis from
digitally pre-recorded diphones or syllables), on the other hand, does not lend itself to
detailed perceptual research, but has been very successful in creating realistic-sounding
samples, particularly in prosodic terms (Rodman, 1999). It is also the most commonly
used speech synthesis type in commercial text-to-speech (TTS) applications. The third
method, LPC analysis/re-synthesis (see the experiment described in 1.4.2.3) produces
very realistic-sounding samples from real speech and allows a certain degree of
manipulation, particularly in terms of formant frequencies, bandwidths, and amplitudes.

There are other, similar methods such as PSOLA (Pitch-synchronous Overlap and Add),

93

which allows the re-synthesis of pitch and duration. LPC and PSOLA can be used in
tandem. Since both LPC and PSOLA crucially rely on voiced samples, they are,

therefore, most effective with voiced speech.

3.2.5.2 The th and st continuum

The choice of speech synthesis methodology was crucial to the success of this study.
Initially, the LPC analysis/re-synthesis method was considered but due to its reliance on
real speech samples it had to be eliminated. It was important to create a stimulus that
contained a very small, highly controlled set of acoustic parameters. LPC-based re-
synthesis produces samples that contain too much speaker-specific, and hence,
uncontrollable, spectral detail. Therefore, a parametric synthesis method was chosen as
the primary tool in the production of target word stimuli. A parametric synthesizer allows
the researcher to produce realistic-sounding samples, while controlling all of the acoustic

parameters involved.

A 7-step /a/~/m/ (th and st)15 continuum was synthesized with the HLsyn parametric
synthesizer (Sensimetrics, 1997), which is based on the Klatt synthesizer (1990). Since
the study focused on the fronting of /a/, the synthetic vowel continuum was created along

F2, with appropriate formant transition adjustments for the preceding and following
consonants. The continuum was based on real formant data measured from two young,

middle-class males — one from Lower Michigan (Talker LM) and one from The Upper

 

‘5 th and st will be henceforth used to mean “h + vowel + t” and “s + vowel + k” respectively.

94

Peninsula (Talker UP) (see also 3.2.5.3 for detailed information about the two talkers).

The synthesized vowels had the following properties:

1. F1) — falling from 120 to 100 Hz.
2. F1 - fixed at 750 Hz (mean value of Talker UP and LM’s /a/ and /m/).

3. F2 —— 1243-1441 Hz (33 Hz intervals)”.
4. F3 — fixed at 2500 Hz, with formant transition adjustments.

A speech continuum is a common tool is speech perception experiments (see, for

example, 1.4.2.3 and 1.4.4.3). Figure 45 shows first and second formant tracks of each of

the seven steps of the /a/~/$/ continuum in “sock~sack” used in the study. As can be

seen, F1 is kept constant, while F2 increases incrementally (by 33 Hz) from 1243 Hz to

1441 Hz (along the top of Figure 45). At some point in the continuum, the vowel moves

across the contrastive (phonemic) categories of /a/ and /&/ (indicated by the arrow along

the top). The exact point at which this occurs was the main focus of this investigation. It
was surmised, based, for example, on the Ladefoged and Broadbent (1957) study, that the
category cross-over point (see 3.2.7.2 for a detailed discussion) might be different
depending on the stimuli involved (such as the dialect of the speaker of the precursor
phrases in 3.2.5 .3), though the exact nature of this relationship was unknown. Also, note

that the formant tracks exhibit shapes characteristic of formant transitions coming out of

 

'6- This continuum was initially synthesized in ll-steps.A pilot study helped eliminate 2 lowest and 2
highest of the original steps because responses had plateaued prior to these endpoints of the sequence.

95

the preceding consonant /s/ and into the following consonant /k/. This was done in order

to obtain a naturalistic sample.

 

1440- ‘ F2

 

 

 

g 1240‘ \I’
<1)
:3
g 750 A A A A AAA Fl
LL.
1 2 3 4 5 6 7 5.61,
0 I 1
Time (ms) 200 "‘5

Figure 45. F l and FZ tracks of the 7-step continuum of “sock-sack” used in the
experiment

Figure 46 shows a spectrographic contrast between steps 1 and 7 in /st/ syllable
context, while Figure 47 contains LPC spectra obtained from the same pair of vowels.
Note that the primary spectral differences between these two steps are mostly in the
absolute values of F2 in Hertz. As can be see both in the spectrograms and in the LPC
plots, the synthetic samples have speech-like properties. The spectrograms, for example,
show a fair amount of frequency perturbation characteristic of speech while the LPC
spectra show realistic-looking spectral envelopes with well-defined formants, narrow

bandwidths, and properly attenuated formant amplitudes.

96

Step 1

 

 

 
  

 

 

 

 

 

 

 

 

 

   

 

 

5000, j
| I,
10211101111201: ;
0 1 ‘, I i ' . 77
"1
1' it
1; m
:1: F s 71‘ '71 T 7 T T ,
v
3 0.442482 0.442482
1':
D 51.01.
a .‘
a _ . $-
”- fl’tinlh‘lwti'im' -*
,l .2
0442482 . 2482
Time(s)

Figure 46. Spectrographic images of the first step and last step of the /a/~/a:/ continuum

70.43

 

50.43

 

30.43

 

 

Sound pressure level (dB / Hz)

 

 

 

 

 

1 0.43
0 1 000 2000 3000
Frequency (Hz)

Figure 47. LPC spectra of the first and last step of the continuum

97

3.2.5.3 Preliminary experiment
Prior to participating in the main experiment described in the subsequent sections of this
chapter, each subject was asked to participate in a short, preliminary experiment designed

to test the subjects’ /a/~/m/ category boundary as a function of single-word stimuli. The

stimuli representing the 7-step /a/~/m/ continuum for the pairs “hot~hat” and “sock~sack”

were presented in a forced-choice mode (see 3.2.6 for more details). An ANOVA test
revealed that the values of category boundary cross-over points were not significantly

different for the two groups of respondents (F (l ,l6)=.06, p=.81). This indicates that even

though there were phonetic differences between their vowel systems (/a/, /a=:/, and /£/, in

particular), both groups shared a similar, general representation of the /a/~/2e/ category

boundary. It was, therefore, of a great deal of interest to see whether this boundary would
shift if the target words were preceded by precursor phrases spoken by talkers

representing different dialect regions.

3.2.5.4 Precursor phrases

Two sets of four semantically neutral phrases were recorded with a close-talking, flat-
response microphone (Sennheiser HMD25-l) and a Tascam DA-Pl DAT recorder at

48,000/ 16 bit, one set by Talker LM and one by Talker UP. The precursor phrases were

designed to contain a broad sampling of the talkers’ vowels and to include specific

exemplars of /a/ and /ae/. At the end of each phrase, the talkers were asked to say the

syllable “uh” in order to complete the phrase prosodically. There precursors were as

follows:

98

1. Bob was positive that he heard his wife. Shannon. say "uh."
2. Cathy's card was blue and said: "pot", while Mary's was black and said: "uh."
3. The key to winning the game of boggle is to know lots of short words like "uh."

4. It turned out that the most common response to question thirty two on last week's test
was: "uh."

Talkers LM and UP were of similar age and F0. They were selected carefully to
minimize possible VTL normalization effects. Their vowel systems, as shown in Figure

48, were similar, except for the NCCS-influenced vowels /a/, /a/, and /8/”. As evident
from Figure 48 and Table 7, Talker LM had a vowel system showing advanced stages of
NCCS in ways consistent with Labov (1991), except for /ae/-raising. The arrows point

from Talker UP to Talker LM to illustrate the NCCS movement of /a/-fronting (+366

Hz), /ae/-fronting (+380 Hz), and /e/-lowering (+207 Hz). These are substantial, but not

unrealistic, differences. Also, note that the rest of the vowels in the chart are in close
proximity to one another, which, most likely, indicates both similar dialectal features and

similar VTL properties of the two speakers.

 

'7 Formant data were obtained from the wordlist in APPENDIX F

99

 

 

 

 

 

20

 

 

 

 

400. ....................... ............ . ...........................
’r? i
E 600 .................. _ ..... ..... ..................................... ....... .......................................
k 3 i E
800- ............... . ....... . ....... . ..... ........................
O Talker UP 2 i
[:1 Talker LM
1000 i i i
3150 2537.5 1925 1312.5 700

F 2 (H2)
Figure 48. Vowel systems of talkers LM and UP

 

 

 

Talker LM Talker UP
Vowel F1 F2 F1 F2
/i/ 300 2210 312 2190
/I/ 478 1999 448 1850
/g/ 717 1712 510 1662
/ae/ 732 1940 750 1560
/a/ 803 1525 692 1159
/3/ 630 1 152 671 1205
/U/ 549 1261 488 1298
/u/ 314 1407 305 1349
/A/ 692 1330 671 1363

 

Table 7. Talker LM’s and UP’s mean F1 and F2 values in Hertz

100

3.2.5.5 Finalized stimuli

Finalized stimuli consisted of a carrier phrase with one of the target word variants at the
end (Figure 49). The original recordings of Talkers LM and UP were downsampled to
11,025 Hz to match the sample rate of the synthetic target word. The real and synthetic
speech samples were RMS (root-mean-square) peak-level normalized to ensure uniform
levels throughout the stimuli. The samples were then merged and checked for potential
problems, such as digital clicks. It was crucial that the synthetic and real speech samples
be as close to each other in sound quality as possible. This is why special care was taken
to acquire flat-response recordings with as little extraneous noise as possible. Figure 50
shows a spectrogram of a fragment of precursor phrase I (see 3.2.5.3). The formants
(dark bands) appear visibly strong and have narrow bandwidths. There is no evidence of

unwanted noise or spectral bias in this recording.

Precursor phrase by Talker LM or UP Synthesized target word

 

B’ob was positive that he heard his wife, Shannon, say: 71%

—-—-———-———————————-—————————-—-—————-——————————————————-—d

Figure 49 Finalized stimulus: a UP or LM precursor phrase with a synthesized target th
or st word at the end

101

No noise [Strong formants
I

 

  

 

 

 

 

 

5000

”a
E
6‘
5
§
13:

0 l 1 . .,,111

b 1a] pl a ] zmv ] aarhilhadhrzf
0

Time (s)
Figure 50. Spectrogram of a carrier phrase fragment by Talker UP

3.2.6 The experiment

3.2.6.1 Forced-choice experiments — single and multi-factor designs

Many of the early categorical perception studies (see detailed discussion in 1.4.4.3) were
based on a single-factor, forced-choice design. In these types of experimental conditions,
all variables, but one are eliminated or made neutral, and only the target variable (such as
voice onset time, or VOT) is systematically changed across the stimuli (e.g., Abramson &
Lisker (1968)). Such stimuli, typically on an n—step continuum, are presented to listeners
randomly and, at each trial, the subjects are asked to choose one of the two possible

contrastive categories (e.g.. /p/ or /b/). The most important advantage of a single-factor

design is that the researcher is able to control the input variable very well. At each trial,
the experimenter knows which specific acoustic parameter triggered which specific
perceptual response. However. single-factor experimental conditions offer a limited
model of the real world, where multiple factors vary simultaneously. Therefore, even if

respondents do exhibit significant behavioral patterns, these patterns often cannot be

102

directly attributed to patterns existing in the real world. Massaro (1998) argues that such
experiments are likely to investigate fimctional cues, or cues whose functional value in

perceptual ecology is not as important as single-factor experiments would often indicate.

Multi-factor experiments, on the other hand, simultaneously manipulate several cues,
which makes the experimental situation more consistent with the real world. In some
experiments, cues vary within one modality, while in others, they span across different
modalities (see, for example, 3.1.1.3). Another important advantage of multi-factor
experimental design is that the investigation of need deals with a much larger data set
with more inter-dependent variables. It can thus be argued that such large and diverse
data sets provide a better empirical coverage of human behavior, thus, potentially,

allowing more robust theoretical claims (Massaro, 1998).

3.2.6.2 The laI-fronting study as a multi-factor experiment

The /a/-fronting experiment was of a multi-factor, forced-choice design. While all cues

varied only within the auditory modality, they simultaneously varied across:

I . target word (2 levels — “hot~hat”, “sock~sack”)
2. target word F2 (7 levels — see the st continuum in Figure 45)
3 . precursor phrase dialect (2 levels — Talker LM and UP)
4. precursor phrase type (4 levels — see the precursors phrases in 3.2.5.3)

If one assumes that the speech signal contains a great deal of simultaneously varying

auditory cues and that it needs to be normalized in order to be processed effectively (e.g.,

103

Ohala (1981)), then the experiment provided listeners with at least 4 types of such
acoustic-phonetic cues and obliged them to employ a normalization process more
complex than that present in single-factor categorical perception experiments. One could,
therefore, argue that, at least from the theoretical point of view, the experimental design

of the present study would allow obtaining empirically and behaviorally viable findings.

3.2.6.3 Experiment delivery on a laptop computer

Variationist sociolinguistic research has been dominated by fieldwork (Chambers, 1995).
Traditionally, sociolinguists prefer to collect their data at a place of the subject’s own
choosing, such as their home or place of employment. The argument of observer’s
paradox (Labov, 2001) demands that fieldworkers not create an atmosphere in which the
authenticity and naturalness of the language sample would be compromised. Still, some
believe that certain micro-level phonetic dialectal features are beyond the level of talkers’
conscious interpretation and evaluation and should, thus, not be subject to variation vis-a-
vis observer’s paradox (Labov, 2001). Speech science research, on the other hand, has
not been preoccupied with observer’s paradox to the same extent, and most experimental
research has taken place in speech laboratories where the subjects’ awareness of
experimental conditions is emphasized but where experimental conditions can be
controlled very well. While designing a sociophonetic speech perception study, one had
to observe the main principles of sociolinguistic field research while providing controlled
and rigorous experimental conditions. Therefore, a compromise was reached whereby the
experiment was to be run on a modern laptop computer at a quiet place of the subject’s
choice. Some respondents chose to have the experiment run at their own house, while

others chose a public library quiet study room or a vacant conference room.

104

The advantages of running a speech perception experiment on a modern laptop computer
are undeniable. First of all, it was possible to move the experiment out of the speech
laboratory, which enabled the investigator to travel to Detroit, MI and Ishpeming, MI in
search of eligible respondents. Also, the 16-bit audio capability of a modern laptop
computer is close in quality to that of older speech laboratory stationary workstations.
While it is true that many built-in computer audio chips produce certain levels of noise
from electrostatic interference (Pohlmann, 2000), this noise is audible only at volume
levels far greater than those used in the study. Volume optimization was an integral part
of the experiment’s design. Initially, it was believed that the relatively low level output of
the laptop’s D/A converter would not be adequate to drive high-quality, 60 Q
headphones. A portable Rolls 4-channel stereo headphone amplifier was used to
experiment with different volume levels. However, it was finally determined that the
built-in audio chip provided sufficiently high volume amplification for optimal listening

experience.

Perhaps the most serious disadvantage of delivering the experiment on a laptop computer
was the fact that touchpad keys had to be used as a response input device. Computer
keyboards have a resolution of approximately 20-35 ms, while specially designed
response pads can achieve latencies of approximately 1 ms. The laptop computer did not
have a functioning serial port, which prevented the investigator from using one of the
commercially available response pads. In the near future, USB response pads are
expected to be manufactured, which will make them compatible with most modern laptop

computers.

105

At the start of the experiment, the subjects were seated in front of the screen wearing
closed, flat-response headphones (Koss R80), necessary both for high quality audio

reproduction and for environmental noise attenuation. When presented with a stimulus,

the respondent’s task was to decide whether the target word sounded more like /a/ (as in

“sock”) or /ee/ (as in “sack”) and to report their choice by pressing the appropriate button

on the computer’s touchpad. At each step of the experiment, the subjects were
automatically given instructions and prompts on the computer screen. The randomized
stimuli were presented in eight blocks of 56 trials and, within a block, each stimulus was
repeated four times. The blocks were divided into two parts with a 30 minute break in

between. Each subject responded to a total of 448 trials.

3.2.7 Analysis and results

The results of each trial were written to a database in real time. Each trial was coded for
reaction time”, dialect of precursor phrase, word, phrase, etc. The data were then
dynamically merged with demographic information about the subjects into a comma
delimited non-proprietary ASCII data file compatible with statistical analysis software
and appropriate for long-term preservation. The data were cross-tabulated for each
subject to obtain information about individual differences. A psychometric function was

plotted for each subject and for each subject group.

 

'8 Only accuracy data were analyzed for the present study.

106

 

3.2.7.1 Psychometric functions

The next step in the analysis was to sub-divide the overall perceptual data set in a
principled way and make a psychometric comparison of the parts. The 7-step stimulus
sequence moved incrementally in physically equal steps of 33 Hz. Psychometric

functions such as those in Figure 51 and Figure 52 are plots of the probability that a

listener will hear the vowel /a/ or /a=:/ at each step in the continuum. In the functions

shown in the right panel of Figure 51, for example, /a/, as in “hot” or “sock,” was heard

approximately 100% of the time at steps 1 and 2, and then a progressively declining

percentage of the time thereafter. The percentage of /a=:/ judgments is complementary,

starting at zero, and then incrementing gradually to 100% by step 6-7.

Psychometric functions were plotted for each group of respondents by each stimulus
type. Figure 51 shows a side-by-side view of the mean responses given by the LM

subjects depending on whether the precursor phrases were spoken by Talker LM or
Talker UP. The psychometric functions can be seen to be “well-formed” with the/a/~/&/
response functions changing monotonically along the stimulus continuum. Well-formed
psychometric functions are a prerequisite for further investigation of the results. The
cross-over point (marked by the dashed line) between the categories /a/ and /a=:/ is
different for each type of precursor phrase. It is approximately at step 4 for UP
precursors, and about 5.5 for LM precursors. Hence, this group shifted their category

boundary perceptions by about 1.5 steps along the F2 continuum in response to the

change of precursor talkers.

107

LM respondents, LM precursors LM respondents. UP precursors

 

 

 

l ‘ 100 100
: /.,..._.__.,,~_,.--.

E 80 . 30 1
i 3

= t 60 1 g

i a: ' m
i 40 7 ~ 40 g

i 3

i 20 r 20 8

: m

 

 

 

 

 

 

 

Stimulus value

Figure 51. Psychometric functions of LM respondents to the stimuli with LM precursors
and UP precursors

In contrast, the UP respondents, whose mean responses are given in Figure 52, showed
only a minimal shift based on the change of precursor talkers. Their category cross-over

points were at approximately steps 4.75 and 4.5 for the UP and LM precursors,

 

 

   

 

 

 

   

 

 

 

 

 

respectively.
UP respondents. LM precursors UP respondents, UP precursors
: 100 v 100
,,A/’7 l
E ' 80 i " 80 "U
i : 3;
5 ~60 “in s ”5°34
5 1' ~01
. + .
l - 40 it .1 : ’ 40 a
s a E
/ : - 21) : ’ 20 g
r r 1 I 1 r 1 r 0 I I I L f I I r O
7 6 5 4 3 2 l 7 6 5 4 3 2 l

Stimulus value

Figure 52. Psychometric functions of UP respondents to the stimuli with LM precursors
and UP precursors

3.2.7.2 Cross-over points

The notion of the psychometric “cross-over point” is crucial to the understanding of this
study. The plots in Figure 51 and Figure 52 show the stimulus values increasing

incrementally right-to-left, unlike most mathematical plots. The x-axis was reversed in

108

order to illustrate the “fronting” F2 movement of /a/ towards the category /ae/. The

synthetic target words differed from one another by 33 Hz along each step of the F2

continuum. At each trial, the subjects chose either the category /a/, if they thought they
heard the word “sock” or “hot” or the category /ze/, if they thought they heard the word

“sack” or “hat.” The /a/~/a:/ category cross-over points are, therefore, a measurable and

quantifiable index of the respondents’ “shifting” perceptions across the two categories

 

 

(Table 8).
Stimulus type Listener’s region Mean stimulus value Std. deviation
hot/hat + LM precursor LM 4.900 1.2135
UP 4.113 1.8857
hot/hat + UP precursor LM 3.756 1.0596
UP 3.988 1.4597
sock /sack+ LM precursor LM 5.850 .5579
UP 4.787 .7107
sock /sack+ UP precursor LM 4.844 .8263
UP 4.489 .5079

 

Table 8. Mean stimulus values by stimulus type and listener region

3.2.7.3 Choosing a statistical analysis

The cross-over points were identified for each subject in each experimental condition.
These values were then analyzed statistically with the General Linear Model (GLM),

Repeated Measures (RM) ANOVA procedure (SPSS) to test the hypothesis that cross-

109

over points varied across the two groups of respondents depending on the dialect of the

precursor phrase.

3.2.7.4 The factors

Factors in the ANOVA were “respondent group” (LM respondents or UP respondents),

“precursor talker” (Talker LM or Talker UP), and “word” (“th” or “st”).

3.2.7.5 Results of the AN OVA test

Significant main effects were found for word F(1,16)=9.543, p<.025 and for precursor
type F(1,16)=15.079, p<.001. There was also a significant interaction and for the

respondent group and precursor type, F(1,16)=6.789, p<.05.

3.2.7.6 Follow-up paired-samples t tests

Bonferroni-corrected paired-sample t-tests were performed to test the nature of the
relationship between the respondents’ region and their responses under the different
experimental conditions. It was found that LM respondents gave significantly different
vowel identity judgments as a function of the precursor phrase, t(8)=4.344, p<.025,
unlike the UP respondents, whose responses were not significantly different, t(8)=.96l,
p=.365. The dashed line in Figure 53 indicates the overall responses of the UP group. The
mean stimulus value (category cross-over point) is slightly smaller for UP precursor than
for LM precursors. However, this difference is not statistically significant, and the line
appears almost horizontal. The LM group (solid line) gave very similar vowel identity
judgments to the target words preceded by UP precursors, but they significantly shifted

these judgments when the synthetic target word was preceded by a precursor spoken by a

110

member of their own speech community. The solid line in Figure 53 runs at an almost 45-

degree angle, indicating a significant change in the perceptions of the /a/~/a:/ category

boundary.

5.6

 

.U‘
b
n

.U'
N
1

5.0-1

 

Estimated Marginal Means
A
00

 

 

 

 

4.6
11- ______ Listener’s
4-4 ““““““““““““ region
___________ 3
4.2 “'5” LM
----- UP
4.0
LM UP

Precursors

Figure 53. LM and UP responses as a function of the precursor phrase

3.2.7.7 The word effect

Next, the word effect was investigated. Overall, the pairs hot/hat~sock/sack triggered
significantly different responses, I(17)=-3.118, p<.025. Figure 54 shows a graph of mean
cross-over points by respondent region and by word. Responses to the sequence “th”
(left panel) were different from those to the “st” continuum (right panel). However,
despite the differences, these two sequences show very similar relative patterns across the
two groups of respondents. The significant word effect remains unexplained. Attempts to

correlate it with real-world F1 and F2 of “hat” and “sack” did not yield definitive results.

111

Future study of this effect could be done by studying the effects of initial consonant

duration and formant transitions into the following vowel.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LM res .
6-0 DUPprecursors " ’ . " ,, * ' ' p“ ,
5.5 . , p E] LM precursors
E 50 _, .
§ UP resp.
g 4.5 .. .. ..... .. ,
E UP resp.
‘5 4.0 i .. . .
m
3.5 I l m '
3.0 .
hot/hat sock/sack

Figure 54. Summary of the word effect

3.2.7.8 Precursors

There were four different precursor phrases used in the study (see 3.2.5.3). It was of
interest to see whether the results were similar for all four precursor phrases. The
precursors varied in overall length and, more importantly, in the phonetic environment
immediately preceding the synthetic target word, which could potentially influence the
degree to which dialectal information was conveyed to the listener. It is an open question
as to how dialectal content was conveyed by the precursors, and how much change in
auditory input was necessary to “trigger” a significant perceptual response (see, for
example, 1.4.2.2). Those are, after all, some of the most interesting questions in
sociophonetics. All four precursor phrases were found to trigger very similar perceptual
effects. Statistical analysis revealed no significant difference among them. The circle in

Figure 55 represents precursors spoken by Talker UP and the arrow represents precursors

112

spoken by Talker LM. Pl through P4 indicate the four different precursor phrases. The
numbers 3 through 6 along the bottom of the illustration denote the middle portion of the
7-step continuum. The length of the arrow shaft indicates the difference in response to the
target words preceded by UP and LM carrier phrases. In the case of UP listeners (right),
the arrow shafts are consistently short, while in the case of the LM subjects, they are
consistently long, indicating that, while the overall perceptual effect persisted across the
two groups of respondents, no specific precursor phrase made a greater or lesser
contribution to the results. These results suggest that the perceptual effect can be elicited

fully by any precursor that offers a reasonable sampling of a talker’s vowels, particularly

/m/, /a/, and /e/.

 

 

 

 

 

 

 

LM Listeners UP Listeners
4—0 Pl *0
<———€) P2 +0

4———© 133 Q
{—6) P4 <6
0 Talker UP
0 Talker LM

 

6 5 4 3 6 5 4 3

Stimulus Value

Figure 55. Precursor phrases 1 through 4 (Pl-P4) - not significant in vowel identity
judgments (From Rakerd and Plichta (2003) with permission)

ll3

3.2.8 Summary

The research questions of 3.2.1 (repeated here) were evaluated with empirical research

and quantitative analysis.

1. Does talker-dependent, sociolinguistic information influence speech perception?
2. Do hearer-dependent, sociolinguistic factors influence speech perception?

3. What is the nature of talker normalization involved in this process?
4. Is /a/-fronting perceptually salient among NCCS speakers?

The short answer to questions 1, 2, and 4 is “yes.” Answering question 3 is more

complex. Below, is a summary of the major findings of the /a/-fronting study.

3.2.8.] Summary point #1

Dialectal information contained in the precursor phrase plays a significant role in vowel

identity judgments. Undoubtedly, LM respondents were influenced by the dialect of the

precursor phrase. They shifted their perception of the /a/~/m/ boundary toward higher F2

frequencies, or toward the front the two—dimensional vowel space.

3.2.8.2 Summary point # 3

As evidenced in 3.2.7.8, specific precursor phrases overall were not significant.

114

3.2.8.3 Summary point # 4

The pairs hot~hat and sock~sack triggered significantly different responses. However,
these responses were relative to each group of respondents’ overall vowel identity

judgments.

3.2.8.4 Summary point # 5

In single-word conditions, respondent region was not significant. This seems to indicate
that the LM and UP speech communities shared a general contrastive inventory of /a/ and
/m/, but that the range of phonetic (or allophonic) representations available to the Detroit

community was significantly broader than that of the UP community (see 4.3.2 for a
discussion on the role of multiple allophonic representations in a sociophonetic model of

sound change).

115

CHAPTER 4
ON HEARER-MEDIATED SOUND CHANGE

4. 1 How DO DIALECTS DIFFER?

One of the fundamental questions in sociolinguistics is how dialects differ from one
another. Inevitably, this question leads to the more specific problem of sound change.
Why is it that certain speech communities develop certain pronunciation patterns? In the
field of sociolinguistics, this question has been approached primarily from the point of

view of speech production, with an emphasis on the role of the speaker.

4.1.1 Speaker-mediated sound change in dialectology.

Most typically, sociolinguists assume that the inherent linguistic variability and social
aspects of language use (particularly non-standard usage), are the main forces behind
sound change. Some sociolinguists believe that sound change occurs as a result of
language use economy or ease of production (Kroch, 1978), while others argue that it

evolves directly from the talkers’ linguistic competence. Chambers (1995) argues:

“Where change is involved, a certain variant will occur in the speech of
children, though it is absent in the speech of their parents, or, more
typically, a variant in the parents’ speech will occur in the speech of their
children with greater frequency, and in the speech of their grandchildren
with even greater frequency. The logical conclusion, as time goes by,
will be the categorical use of that new variant and the elimination of older

variants” (p. 185).
4.1.2 The need for a broader model of sound change
While one has to agree with Chambers’ scenario, one must also note that this model

misses one, crucially important, element — the bearer. For any viable sound change

116

model, there need to be at least three elements (1) the speaker, (2) the hearer, and (3)
language variation in the speech community. Therefore, a satisfactory research paradigm
would have to be set at the interface of speech production (the speaker’s role), speech
perception (the hearer’s role), and sociophonetics (language variation in the

acoustic/phonetic domain).

4.1.2.1 Speech production and linguistic variability

It is no secret that sociophoneticians think of the articulatory-acoustic properties of
language use very seriously. While this may have little appeal to formal linguists, it

certainly is an interest shared with speech scientists.

Speech scientists think of speech production as a complex system of rapid articulatory
gestures, subject to physical limitations of human physiology (e.g., Faber (1992)). This
physiological aspect of speech production is crucial to the understanding of the role of the
speaker in sound change, as human physiology is believed to be the most important

source of variability (see, for example, the discussion on VTL variability in 3.1.1.1).

Sociolinguists take a different approach. They do believe in the abstractness of language
and linguistic representation. For example, vowel shifts are described as movements
within the two-dimensional space delimited by objective formant values, but classified by
theory-dependent binary representations, such as “tense” versus ”lax” (Labov, 1991).
Sociolinguists also think of variability as a constituent of sound change, but instead of

focusing on physiology, they are more interested in socially constructed patterns of

117

linguistic variability. Some also believe that this type of variability can be predicted from

a general linguistic theory (e.g., Labov (1994)).

4.1.2.2 Speech perception and linguistic variability

Most speech perception theories agree that the relationship between the acoustic stimulus
and a related auditory response is not linear (see 1.4.2.2). They also believe that speech
perception is, to a large degree, categorical (see 1.4.4.3). Sociolinguists take a different
approach. Perceptual dialectologists study beliefs and attitudes while sociophoneticians

are most interested in “within category” perception (see 1.4.2.3).

4.1.2.3 Why current models cannot account for nasalization and sociophonetic
perceptual category boundary shifting

4. 1.2.3. 1 Problems with nasalization

As evidenced in CHAPTER 2, coarticulatory vowel nasalization occurs as a result of
specific gestures of the human speech production apparatus. It can be measured, either
from spectra, or aerodynarnically, and its acoustic properties can be predicted from a
general theory of speech production. Coarticulatory vowel nasalization in Michigan has a
physiological reality (but is non-contrastive and occurs in non-pathological speech and in
non-nasal environments) and a sociophonetic reality (but it has no place in existing
sociolinguistic taxonomy). The problem becomes even more complex in light of the fact

that nasalization can be predicted from talker participation in NCCS.

4. 1.2.3.2 Problems with sociophonetic perceptual category boundary shifling

The perceptual category shifting depending on dialectal input (CHAPTER 3) is also

problematic for current speech and language theories. As you recall, only Lower

118

Michigan respondents shifted the /a/~/2e/ boundary as a function of dialectal input.

Speech perception theories cannot fully account for talker normalization mediated

through the hearer’s speech community.

Sociolinguistic theory has trouble accounting for this phenomenon as well. This
particular type of perception cannot be, for example, explained by folk linguistics (in
ways that Niedzielski’s study cited in 1.4.2.1 could). One cannot attribute the results to

the perceivers’ beliefs about standardness or correctness.

4.2 NASALIZA TION, /a/-FRONTING, AND SOUND CHANGE

4.2.1 Articulatory variability and sound change

Vowel nasalization and perceptions of /a/-fronting in Michigan are correlated with

NCCS. These two studies provide evidence that sound change has a significant hearer-
mediated component. Even though this idea is not new to speech science, there has not
been an account that would consider variability in broader, sociophonetic terms. For
example, Ohala (1981, 1993, 1996), suggested that sound change occurs when listeners
misapply corrective rules’that serve to correct phonetic variability. He defines variability

as “noise,” or variability primarily due to articulatory factors.

Faber (1992) is distrustful of Labovian sociolinguistics and argues that before phonetic
variants can spread, they must first be salient and reproducible. Faber also seems to think
of acoustic variability only in a purely articulatory/phonetic sense and attributes sound
change mostly to the relationship between articulatory variability and categorical speech

perception.

119

4.2.2 Vowel nasalization and perceived height

The first formant frequency in Hertz, as relative to tongue height, is often referred to as
“vowel height.” Several studies in speech perception (e. g., Beddor, Krakow, & Goldstein,
(1986)) have demonstrated that perceived vowel height does not always correspond to the

actual value of F 1. This is particularly true of nasalized vowels.

Krakow et al (1988) demonstrated that listeners can be confused by the similarity of
nasalization and tongue height effects. Krakow and her colleagues used synthetic speech
samples with varying degrees of velopharyngeal opening to simulate the effects of
changes in tongue height. They also discovered that extra coarticulatory effects from the

preceding and following environment influenced the perceptions of vowel height.

Beddor and Hawkins (1990), in a series of perceptual experiments with synthetic speech,
discovered that vowel height is determined by the most prominent harmonics in the low-
frequency region of the spectrum, as well as by the slopes of the skirts of those
harmonics. This finding implies that the relationship between nasalization and perceived
vowel height is continuous. The stronger the nasalization, the higher the amplitude of
F1“, the more significant a role it will play in perception. Similarly, when formant
bandwidths are wide, perception will be determined more by the shape of the spectrum

than by any specific low-frequency harmonics.

Beddor and Hawkins discovered that listeners would always average their perception

between the first oral and nasal formants. This, of course, means that low vowels (such as

/ae/) would be perceived higher, and high vowels such as /£/, would be perceived lower.

120

Beddor and Hawkins have also found the critical distance of 3.5 Bark to play a role in the
perceptions of vowel height. Kingston (1991) provided additional support for the claim
that perceived vowel height is a function of several covarying articulations. He presented
quantitative evidence for the fact that nasalization and F1) are perceptually integrated with

the acoustic effects of tongue height.

The findings obtained in CHAPTER 2 and the perceptual experiments discussed above

can be summarized as follows:

1. Perceived vowel height is not a sole function of F1. It is influenced by a bundle of
covarying articulatory gestures, out of which the opening of the velopharyngeal port
is the most prominent.

2. Vowel nasalization in Michigan is distributed along NCCS lines of region and sex.

3. In NCCS, /&/ is considered raised, and /c/ is considered lowered.

4. The presence of a nasal peak may cause the NCCS-influenced /ae/ to be perceived

higher and /e/ to be perceived lower — subject to auditory constraints of the critical
distance.

5. The simultaneous raising of /a:/ and lowering of /e/ is bearer-mediated in NCCS.
In the course of this chapter, evidence has been provided to support the claim that certain
NCCS vowel movements, such as the raising of /a=./ and the lowering of /e/, are mediated

through the hearer’s perceptions of vowel height. Figure 56 ‘shows a Principle

Components Analysis vowel plot of normalized NCCS vowels collected from 26 Lower

Michigan talkers. Note that the vowels /e/, /ae/, and //1/ share a fair amount of

discriminant space. The F l/F2 classificatory system has failed, but one still must be able

to account for the perceived contrast between these three vowels (also see Figure 42).

121

Clearly, there must be other articulatory-acoustic features that make it possible for

hearers to unambiguously identify these vowels.

 

1275

 

755

 

F1 (Hz)

1015

 

 

 

 

 

 

495
2581 1899 1218 537

F 2 (HZ)

 

Figure 56. Normalized Principal Components plot of an NCC S population from Lower
Michigan

At this stage, one can formulate the claim that nasalization is actively re-shaping the
NCCS vowel space. Nasalization and tongue position are both integrated in perceived

vowel height. The F 1 /F.2 taxonomy (Figure 56) is not able to capture this coarticulatory

process. Speakers reproduce their own auditory perceptions of the vowels /$/ and /e/ by

simultaneously opening the velopharyngeal port and changing tongue height. It might be
possible that, currently, speakers and hearers are using both strategies to negotiate the

desired perceptual effect.

122

 

4.2.2.1 Will nasalization prevail?

It is not unreasonable to argue that the NCCS vowel system will not end up consisting
exclusively of nasal vowels. It is no accident that nasal vowels are much less common in
world languages than oral vowels. Wright (1986), for example, demonstrated that, in
experimental conditions, all-nasal systems suffered a significant reduction in vowel
contrast. Still, nasalization can be expected to continue playing an important role in

altering the vowel space in NCCS-influenced dialects of English.

Which occurs first in NCCS, nasalization or oral cavity gestures? While one might never
know for certain, one can speculate that it is likely that nasalization occurs first, as a
coarticulatory process. It probably occurs as a function of both physiological (low vowels
are more likely to be nasalized) and sociophonetic (as a sociolinguistic marker) reasons.
Because of its contrast reducing properties, it may be forcing speech communities to
renegotiate changes in oral articulations (such as tongue height). As a result, a system-

wide vowel shift, such as NCCS, may finally take place.

4.2.3 Hearer-mediated lad-fronting

4.2.3.1 Information integration in speech perception

Integrating top-down and bottom-up cues in speech perception is not a new idea (see, for

instance 3.1.1.3). However, the /a/-fronting study has broadened this concept by adding

to it a strong dialectal component and demonstrating that perceivers rely on integrating
input-dependent sociophonetic content, as well as, mediating their perceptions via the

sociophonetic experience of their speech community.

123

4.2.3.2 Perceptual salience

The concept of perceptual salience is fundamental to the theory of sound change. When
individuals with different dialects live in close contact, they may adopt some of the
features of that other dialect (e.g., Kerswill (1994)). A sociolinguistic theory of dialect
contact or dialect accommodation must be able to account for the fact that only specific
features are perpetuated, while others die out. Most typically, such accounts would be
based on a combination of synchronic and diachronic studies of production data. Some of
the sound change phenomena would be determined by language universals, while others

would be considered unpredictable or idiosyncratic.

The /a/-fronting study provides a quantificational account of perception data. It allows

one to determine the micro-level acoustic/phonetic detail that carries dialectally salient
weight. In addition, we discover that sound change is not driven exclusively by abstract
language universals, but it is determined by the capabilities of the human auditory system

and the hearer’s own dialect history.

4.2.3.3 33 Hertz and abstract features

Labov (1991) discusses a theory of on-going vowel shifts. One of his most important
claims is that certain properties of vowel shifts can be predicted from a set of generalized
principles, such as “low nonperipheral vowels become peripheral.” For Labov, features
such as, +/- peripheral have their measurable acoustic correlates obtained from acoustic
measurements of speech production. For instance, the forward periphery, he argues,

follows the line of F2-2F1 =C, where C is a constant that varies with the speaker.

124

Even if one accepts Labov’s definition of vowel shifts, one still finds it inadequate to

account for the results of the /a/-fronting study. The study demonstrated that vowel

movements are crucially hearer—mediated and cannot be predicted exclusively from
production data. It is the bearer and their speech community who negotiate the acoustic
properties of vowel shifts. In this particular case, the Lower Michiganders were sensitive
to changes in F2 of as little as 33 Hz, while the UP respondents would have required a
significantly larger interval before they could process this input as meaningful. The
sociophonetically relative perceptual nature of absolute formant frequencies
demonstrated by the study provides yet another argument in favor of augmenting existing

theories of sound change by the bearer-negotiated component.

4.3 TOWARD A SOCIOPHONE TIC MODEL OF HEARER-MEDIA TED SOUND CHANGE

4.3.1 Phonological models of nasalization

As mentioned in 4.2, Ohala (1981, 1993, 1996) put forth a model of sound change based
on the negotiation of ambiguity and the interpretation of articulatory “noise” between the

speaker and the hearer. For example, vowels followed by nasal consonants, /vn/, can be

progressively interpreted as /v/ if the listener discontinues the application of a “corrective

9’

rule, necessary for the interpretation of the nasal environment in /vn/. The Ohalan
model, as applied to nasalized vowels, can be summarized in Figure 57 below. When the
vowel /a/ is pronounced in a nasal environment, it is subject to vowel nasalization (the

lowering of the velum), which the listener initially “corrects” for and identifies the vowel

as /a/ and the following consonant as /n/. However, once the listener stops applying the

corrective rule, the vowel is perceived as /5/ followed by silence. Thus perceived vowel

125

nasalization becomes more salient and the listener begins reproducing and spreading the
new variant /éi/. Even though this particular type of /n/-deletion has significant
phonological consequences (the emergence of the new contrastive category an, Ohala

does not believe that there is an intermediate phonological derivation between abstract

representation and acoustic-phonetic output.

 

speaker hearer

 

spoken as /an/ heard as /a/

 
 

1
1
l
l
l
l

 

distorted as /'an/ produced as /2”1/ l
. _ - z - , _- -1

Figure 57. Summary of the Ohalan listener-mediated model of sound change

Hajek (1997) modified the Ohalan model of sound change by adding to it a phonological
derivational component related to the grammatical structure predicted from the theory of
Lexical Phonology. At the first stage of sound change, listeners deal with the ambiguity
of the speech signal by reinterpreting or misinterpreting the nasal environment directly
following the vowel (/vn/). This occurs as a result of language-specific rules and can lead
to further contextual reinterpretation of vowel nasalization until the process becomes

phonetically stable and interpretable at a deeper level of the post-lexical component. As a

result, a phonemically nasal vowel, /5/, may emerge both at deep and surface levels of

grammar.

126

4.3.2 A sociophonetic model

The studies in CHAPTER 2 and CHAPTER 3 provided evidence that there are significant
sources of variability in the speech signal related to sociolinguistic factors such as region
and sex. Both vowel nasalization and the perceptual /a/~/ae/ category shift can also be
predicted from talker participation in NCCS. The two studies showed that the speech
signal is rich in sociolinguistic information and that this information is actively used in
speech production and perception. Figure 58 shows a proposed model of sociophonetic

sound change whereby articulatory gestures and their perceptions are mediated via the

speaker, the bearer, and their speech community. For example, as evidenced in
CHAPTER 2, certain Lower Michigan individuals are likely to pronounce the vowel /ae/
in the word “bag” with the velopharyngeal port open, which is not caused by the presence
of a nasal consonant, as seen in Ohala’s model in Figure 5 7 (articulatory variability), but,
rather, by sociolinguistic forces specific to their speech community (sociophonetic
variability). At the same time, such pronunciations are subject to negotiation and
reinterpretation by the hearer and their speech community as well. As a result, the output

of such reinterpretation may vary — for some hearers it will result in a nasal vowel (e.g.,

/b2Eg/), while for others, the vowel will be pronounced with a lowered F1 (e.g., /brzeg/).

Such vocalic instability is a characteristic feature of early stages of vowel shifts (Labov et

al., 1997; Stockwell & Minkova, 2000).

Similarly, Lower Michigan talkers are likely to pronounce the word “block” with a

fronted F2 (Figure 58), which makes this word sound more like “black” ([blzek]) to non-

NCCS speakers (such as the UP respondents described in 3.2.4). Lower Michigan hearers

127

are able to normalize the increased frequency of F2 (see 3.2.8) and correctly identify the
“shifted” vowel as /a/. At some point in this speaker-hearer negotiation, the fronted /a/
becomes perceptually salient in their speech community (see, for example, the results of

the a-fronting study in 3.2.8), and the previously non-standard, “fronted” pronunciation

[blak] becomes the norm.

In of the studies presented CHAPTER 2 and CHAPTER 3, the respondents’ speech
community acts as a dialectal filter in the speaker-heater negotiation. This filter “allows”
or “blocks” the use (speech production) and comprehension (speech perception) of non-

standard forms by the speaker and the bearer.

 

Articulatory

gesture Speaker Hearer

 

heard as [bzeg[ or [bég]
heard as [blak] or [black]

lam-nasalization intended as ”9338/ "bag"
/a/-fronting intended as /blak/ "block

/

/a3/-nasalization pronouced as [bzég] pronounced as [big] or [bratg]

/a/-fronting pronouced as [black] pronounced as [blak] or [blatk]

   
   
 
 

Speech community

 

 

 

 

Figure 58. Sociophonetic model of hearer-mediated sound change

It is, therefore, not unreasonable to argue that a successful model of sound change must
be able to account for sociophonetic variability (see 4.1.2) regardless of whether it
assumes an intermediate phonological derivational component or not. NCCS speech
communities are reinterpreting and misinterpreting, negotiating and renegotiating vowel
nasalization, tongue height, tongue frontness, and, possibly, a host of other articulatory-
phonetic features of vowels. As a result, the diverse and dynamic speech communities of

the American North, such as Detroit-area European Americans. are quickly adopting the

128

new vocalic changes. NCCS is. therefore, likely to continue playing an important role in

modern American English.

129

PWNP‘MFPPT‘

NNNNN—‘t—‘t—tu—tu—In—a—u—nu—ou—a
#UJN—‘oomNONM-bWN—‘o

jaw!
job
knock
hd
lot
nasty
pot
set

shed

.bob
.shot
.sfi

. kick
.nag
.rnan
.caught
.head
.cod
.coat
.sought
.test
.hut
.boat
.but
25.

bag

26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.

45

APPENDIX A - WORDLIST USED IN CHAPTER 2

back
bk
book
bond
buddy
but
sad
cap
cot
dad
took
lefi
bet
dead
(fid
hat
sat
hh
should

.shut
46.
47.
48.
49.
50.

hook
hot
cat
rnat

cut

Table 9. Wordlist used in studies described in CHAPTER 2

130

APPENDIX B —— QUESTIONNAIRE (CHAPTER 2 and CHAPTER 3CHAPTER 2)

What is your age?

 

What is your current occupation?

 

What are your parents’ occupations?

 

What is your education?

 

Where do you live?

 

Tell me about your neighborhood

 

The most common type of housing here is detached house (164/558). Is this you?

 

Have you moved a lot in your life? Where? Other cities, states, countries?

 

Are there any family members living in this area?

 

Are there many people from your neighborhood in your workplace?

 

Are there more men or women in your workplace?

 

Are there any activities in the neighborhood that you participate in? (sports, park,
community center, gym, etc.)

 

Do you hang out with your work mates after work?

 

Do you like living in your city/town/village?

 

If you have a choice, would you live some place else?

 

What would be one thing you would like to change about this town?

 

131

Region
LM

MM

UP

Sex
female

male

female

male

female

male

Subject Code
LM30
LM3 l
LM39
LM40
LM49
LM50
LM55
Total
LM32
LM34
LM35
LM36
LM5 1
Total
LM37
LM43
LM44
LM46
Total
LM28
LM38
LM41
LM6O
Total
LM33
LM42
LM45
LM47
LM48
LM53
Total
LM56
LM29
LM58
LM61
Total

Mean %N N
22.948 48
23.569 47
38.326 48
18.994 47
25.214 48
26.408 48
21.703 46
25.355 332
18.625 48
11.852 47
14.268 48
25.582 45
13.332 26
16.732 214
12.722 46
15.174 47
20.142 45
18.876 48
16.725 186
9.1647 45
8.9396 48
8.1839 46
8.5272 47
8.703 186
12.219 46
10.753 46
17.558 46
12.505 43

15.78 47
10.683 47
13.258 275
8.6149 47

10.08 47
9.3065 48
9.3156 48
9.3294 190

APPENDIX C — SUMMARY OF INDIVIDUAL CASES OF %N

Std. Deviation
8.901398704
6.648855486
8.931787802
6.337375243
12.20337446

1 1.682937
10.79227997
1 1.14084192
8.701631786
8.159063948

7.671490925
5.943389486
6.0610973 87
7.307334707
7.643 89293
8.905235309
1 1.71 140184
6.292540614
9.232970837
5.280797374
4.830615554
5.278136236
4.769683565
5.013505799
7.144046909
6.503185693
8.68847108
7.587818668
7.294982194
6.991691 064
7.7683 8301 7
5.647302885
6.824183964
5.109943785
5.93 73 1 3278
5.879685978

 

Table 10. Summary of individual cases for %N

132

APPENDIX D — CONSENT FORM CHAPTER 2

Vowel nasalization among Michigan speakers of English
Bartek Plichta, Department of Linguistics and Languages,
Michigan State University

East Lansing, MI 48824

plichtab@msu.edu

tel. 517-3559300

Consent Form

This study is done as part of the research project in the area of speech production in the
context of on—going dialectal changes in the state of Michigan. The purpose of this study
is to investigate how Michiganders produce several different sounds of American
English.

The subjects will be asked to put on a small, head-worn microphone and read a list of 50
simple, monosyllabic words. The investigator will tape record those words. In some
cases, the subjects may be asked to speak to a non-invasive device called a “nasometer,”
which is a microphone with a small, plastic mask around it. '

The subjects will be asked to provide basic demographic information, including their age,
gender, ethnicity, and socio-economic status.

Participation in the study is voluntary. The subjects may refuse to participate at any stage
of the study.

The subjects’ privacy will be protected to the maximum extent allowable by the law.
There is no risk of physical injury involved in the study.

The subjects may contact:

Professor Dennis Preston

at the Department of Linguistics and Languages (Michigan State University)

A-740 Wells Hall, tel. 517 353-9945; email: preston@msu.edu

or Ashir Kumar, M.D., Chair of the University Committee on Research Involving Human
Subjects (UCRIHS)

phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu,

regular mail: 202 Olds Hall, East Lansing, MI 48824.

I have read, understood, and accepted the consent form:

Date Signed

133

 

APPENDIX E — CONSENT FORM CHAPTER 3

Perceptions of /a/-fi:onting across two Michigan dialects of English
Consent Form

This survey investigates perceptions of the fronting of the vowel /a/ (in words, such as
hot, sock, top, lot, etc.)

You will be asked to sit in front of a multimedia computer.

You will read and accept (or not) the consent form.

You will be given brief instructions on how to proceed.

You will be asked to put on headphones. F,
The experiment takes about 45 minutes to complete. ll
The experiment will consist of 2 blocks with a break in between. You will be 1;
automatically notified when the break should occur.

You can interrupt the experiment at any point, should you require an extra break.

In each block, you will hear a short sentence, at the end of which, you will hear the word

"sock" or "sack." Your task is to click the left button if you hear "sock" and the right

mouse button if you hear "sack."

Each block consists of 224 trials.

Participation in the study is voluntary. You may refuse to participate at any stage of the

study.

Your privacy will be protected to the maximum extent allowable by the law.
There is no risk of physical injury involved in the study.

If you have questions about the study, contact:

Brad Rakerd, Professor, Dept. of Audiology & Speech Sciences
373 Communication Arts Building

Michigan State University

East Lansing, MI 48824-1212

phone: (517)432-8195; fax: 432-2370

In case you have questions or concerns about your rights as a research participant, please
feel free to contact Peter Vasilenko, Ph.D., Chair of the University Committee on
Research Involving Human Subjects (UCRIHS)

phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu,

regular mail: 202 Olds Hall, East Lansing, MI 48824.

By signing this form, I volunteer to participate in this study.

YOUR NAME (please print) SIGNATURE

134

APPENDIX F — WORDLIST USED IN CHAPTER 3

 

 

l. jaw 26. move
2. job 27. bit
3. knock 28. book
.4. lid 29. boot
5. lot 30. rude
6. nasty 31.but
7. pot 32. sad
8. set 33.cap
9. shed 34. cot
10. heat 35. dad
11. shot 36. bead
l2.sit 37. left
13. soothe 38. bet
14. nag 39. dead
15. man 40. did
16. caught 41. hat
17. head 42. sat
18. cod 43.hit
19. coat 44. should
20. sought 45. shut
21. test 46. hook
22.hut 47. hot
23. wheat 48. cat
24. but 49. mat
25. bag 50. cut

 

Table 11. Wordlist used in the study in CHAPTER 3

135

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