Sonority has been one of the most debated concepts in phonetics and phonology. Constraintsinvolving sonority such as the Sonority Sequencing Principle (SSP), the Sonority Dispersion Principle (SDP), or the Syllable Contact Law have long been used by phonologists to understand syllable structure (Sievers, 1881, 1901; Steriade, 1982; Selkirk, 1984; Clements, 1990; Kenstowicz, 1994; Parker, 2002, 2011). However, there is no consensus on the phonetic basis of sonority, either in the articulation or the perception of speech (Albert, 2023). This dissertation explores sonority in speech production.Current speech production theories do not predict the variation of gestural coordination relevantto sonority. However, sonority has been observed to be correlated with systematic variation in gestural coordination, based on CC clusters in Georgian (Crouch, 2022). Also, some observations (Gao, 2008; Shaw and Chen, 2019) suggested that sonority seems to be a factor that systematically correlates to CV gestural coordination variation. In my dissertation, I followed up on these previous studies and explored whether there is a positive correlation between sonority difference and CV lag (the gestural lag between a consonant and a vowel) in English and Mandarin. Based on corpus data of English, as well as Electromagnetic articulography (EMA) experiments participated by English and Mandarin speakers, I found that CV lag positively correlates with CV sonority difference in both languages.There are 32 English stimuli from the Wisconsin X-ray Microbeam Database (Westbury et al.,1990) used to test the main claim in experiment 1. Analyzing the corpus data suggested that there is a significant positive correlation between CV lag and sonority difference. To address the limitation of using an existing corpus and to provide a cross-linguistic comparison, EMA data of 24 English stimuli (experiment 2) and 26 Mandarin tone 4 stimuli (experiment 3) were collected and analyzed. Each set of stimuli in the EMA experiments was read 15 times in different randomized lists. When collecting EMA data, sensors were glued to the tongue tip, tongue blade, tongue dorsum, upper lip, and lower lip of each participant. All the kinematic data were annotated in Matlab using the lp_findgest algorithm of the mview package (Tiede, 2005), where the landmarks were labeled at 20 percent thresholds of peak velocity. The CV lag was computed by subtracting the target onset (onset of gestural plateau) timestamp of the consonant from the target onset timestamp of the vowel (Zhang et al., 2019; Durvasula and Wang, 2023). The sonority difference was quantified by subtracting the C sonority from the V sonority using the sonority scale in Parker (2011). Plots and mixed effects modeling was generated in R (R Core Team, 2017) where CV lag was modeled as a function of the sonority difference, with participant, stimuli, and C duration as random intercepts.The finding is that for all the data, CV lag positively correlates to sonority difference significantly.Sub-groups of the stimuli controlled for consonant place of articulation or vowel height mostly exhibited the expected correlations. I also used consonant displacement and vowel displacement as estimates for jaw movement, and these findings suggest that jaw movement may not be a valid alternative account. The dissertation found a positive correlation between sonority and CV gestural coordination in English and Mandarin. If we make an assumption that larger lags are preferred within a syllable, the finding forms a basis to explain universal constraints such as the SSP and the SDP.
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