Structured heterogeneity in Scottish stops over the twentieth century

Abstract

How and why speakers differ in the phonetic implementation of phonological contrasts, and the relationship of this 'structured heterogeneity' to language change, has been a key focus over fifty years of variationist sociolinguistics. In phonetics, interest has recently grown in uncovering 'structured variability'-how speakers can differ greatly in phonetic realization in nonrandom ways-as part of the long-standing goal of understanding variability in speech. The English stop voicing contrast, which combines extensive phonetic variability with phonological stability, provides an ideal setting for an approach to understanding structured variation in the sounds of a community's language that illuminates both synchrony and diachrony. This article examines the voicing contrast in a vernacular dialect (Glasgow Scots) in spontaneous speech, focusing on individual speaker variability within and across cues, including over time. Speakers differ greatly in the use of each of three phonetic cues to the contrast, while reliably using each one to differentiate voiced and voiceless stops. Interspeaker variability is highly structured: speakers lie along a continuum of use of each cue, as well as correlated use of two cues-voice onset time and closure voicing-along a single axis. Diachronic change occurs along this axis, toward a more aspirationbased and less voicing-based phonetic realization of the contrast, suggesting an important connection between synchronic and diachronic speaker variation.*

The Effect of Indian Contact and Glaswegian Contact on the Phonetic Backward Transfer of Glaswegian English (L2) on Hindi and Indian English (L1)

“…Table 1 presents the findings from previous research on VOT in /t/ across Glaswegian English, Indian English, and Hindi. In the word-initial position, Glaswegian English /t/ is aspirated, which is reflected in longer lag/positive VOT (Sonderegger et al 2020;Stuart-Smith et al 2015), as seen in Table 1. By contrast, in both Hindi and Indian English (Table 1), /t/ remains unaspirated and has short-lag positive VOT (Benguerel and Bhatia 1980;Hauser 2016;Lisker and Abramson 1964;Ohala and Ohala 1992).…”

Advancements of Phonetics in the 21st century: Quantitative data analysis

“…Morrison, 2007;Schertz et al, 2015), and production data may describe how acoustic cues map to one of k (allo)phones (e.g. Cole et al, 2010;Davidson, 2016;McCarthy and Stuart-Smith, 2013;Sonderegger et al, 2020). These papers analyze their data using frequentist multinomial models, either using specialized packages for such models or by transforming the data to lme4compatible format.…”

Computational sociophonetics using automatic speech recognition

“…(2015) and Sonderegger et al. (2020) studied voice onset time change in the Glasgow variety of Scottish English using 7350 tokens from 23 speakers, and Stuart‐Smith et al. (2018) used approximately 112,000 force‐aligned tokens of /s/ and /ʃ/ to compare the retraction of /s/ in consonant clusters in American and Scottish English.…”