The private life of stops: VOT in a real-time corpus of spontaneous Glaswegian

Abstract

While voice onset time (VOT) is known to be sensitive to a range of phonetic and linguistic factors, much less is known about VOT in spontaneous speech, since most studies consider stops in single words, in sentences, and/or in read speech. Scottish English is typically said to show less aspirated voiceless stops than other varieties of English, but there is also variation, ranging from unaspirated stops in vernacular speakers to more aspirated stops in Scottish Standard English; change in the vernacular has also been suggested. This paper presents results from a study which used a fast, semi-automated procedure for analyzing positive VOT, and applied it to stressed syllable-initial stops from a real-and apparent-time corpus of naturally-occurring spontaneous Glaswegian vernacular speech. We confirm significant effects on VOT for place of articulation and local speaking rate, and trends for vowel height and lexical frequency. With respect to time, our results are not consistent with previous work reporting generally shorter VOT in elderly speakers, since our results from models which control for local speech rate show lengthening over real-time in the elderly speakers in our sample. Overall, our findings suggest that VOT in both voiceless and voiced stops is lengthening over the course of the twentieth century in this variety of Scottish English. They also support observations from other studies, both from Scotland and beyond, indicating that gradient shifts along the VOT continuum reflect subtle sociolinguistic control.

The VOT Category Boundary in Word-Initial Stops: Counter-Evidence Against Rate Normalization in English Spontaneous Speech

“…The SIQR was not calculated for voiced phonemes, as many of them were assigned a VOT of 0 ms, which in many cases had no numerical significance (see Section 2.2). The median VOT values of the six phonemes at the first level of analysis (all words) were comparable to the mean VOT values of corresponding phonemes in sentence context in Lisker and Abramson (1967), with bilabial stops having the shortest VOT and velar stops the longest (see also Fricke, 2013;Schiavetti et al, 1996;Stuart-Smith et al, 2015). Optimal category boundary locations for the three pairs of homorganic stops were also the shortest for bilabial stops and generally the longest for velar stops, and were roughly within the range of category boundary locations for the three places of articulation reported in Summerfield's (1975Summerfield's ( , 1981 perception studies.…”

“…Whether or not they subscribe to rate normalization views, virtually all production studies report asymmetrical effects of articulation rate on voicing categories, with much smaller effects on short-lag than long-lag categories (Kessinger & Blumstein, 1997;Magloire & Green, 1999;Miller et al, 1986;Nagao & de Jong, 2007;Pind, 1995;Schiavetti et al, 1996;Stuart-Smith et al, 2015;Volaitis & Miller, 1992). Conceivably, for naturally occurring ranges of VOT, a rate-independent category boundary between short-lag and long-lag VOT is effective enough across different rates of articulation.…”

“…This meant that no negative VOT was used; a VOT of 0 ms was assigned to prevoiced utterance-initial stops and utterance-medial stops produced with continuous voicing from before the stop release. This decision was made because the onset of voicing could not be easily determined for a majority of such cases, which were utterance-medial and had continuous voicing from segments before the stop closure (see also Lisker & Abramson, 1967;Stuart-Smith et al, 2015). As we elaborate in Section 2.3, this did not affect the locations of optimal VOT category boundaries or their classification accuracy, our main analysis tools.…”

Voice Onset Time (VOT) at 50: Theoretical and practical issues in measuring voicing distinctions

“…The most widely used system (Keshet, Sonderegger, & Knowles, 2014) has about 80% agreement between manual measurements and the automatic ones within 5 ms, and closer to 90% at 10 ms. This system has been used for an extensive analysis of Scottish English (Stuart-Smith, Sonderegger, Rathcke, & Macdonald, 2015), where they used a manual check on the VOTs before final analysis. They found that 62.6% of the VOT measurements were correct, 15.8% needed to be corrected by hand, and 21.6% were not usable (p. 518).…”

The beauty in a beast: Minimising the effects of diverse recording quality on vowel formant measurements in sociophonetic real-time studies

“…We addressed this question using the example of a subset from an electronic real-time corpus of Glaswegian vernacular speech which comprises of diverse recordings made at different points in time, by different people and for different purposes (including sociolinguistic and oral history interviews as well as free conversations, see 2.1; Rathcke and Stuart-Smith 2015;Stuart-Smith et al 2015). 2 We chose /u/ as a case study into disentangling the technical effects from the sound change, given there exists some reliable external evidence for this vowel in both historical and modern-day Scottish English data (McAllister 1938;Macaulay 1977;Johnston and Speitel 1983;Scobbie 2011;Scobbie, Lawson and Stuart-Smith 2012;Stuart-Smith et al 2016).…”