The private life of stops: VOT in a real-time corpus of spontaneous Glaswegian
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".