Voice onset time variation in natural southern speech
Bibliographic record
Abstract
Sociophonetic research has traditionally emphasized vowels and only recently begun to examine consonants. The emerging literature on consonants has found systematic regional variation in consonant production (Jacewicz et al., 2009; Eddington and Turner, 2017). These studies have focused on present-day speakers producing lab-recorded speech and thus provide little historical or naturalistic insight to consonant variation. One way that the production of stop consonants varies is in voice onset time (VOT). In this study, we utilize the Digital Archive of Southern Speech (DASS) (Kretzschmar et al., 2013), a collection of 64 sociolinguistic interviews recorded between 1968 and 1983, to explore consonant variation in an unscripted historical setting. DASS was force-aligned using the Montreal Forced Aligner (McAuliffe et al., 2017), and the VOT of pre-vocalic, word-initial stop consonants in three-minute audio clips from each speaker was measured using AutoVOT (Keshet et al., 2014). VOT was normalized by dividing duration by speaking rate, and this normalized measurement was included as the dependent variable in a mixed effects model. As expected, stress, voicelessness, and a dorsal place of articulation were significant predictors. Our preliminary analysis reveals significant regional and age differences, adding evidence for speaker-specific variation of VOT as a result of sociophonetic variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".