Effects of following onsets on voice onset time in English
Bibliographic record
Abstract
Voice Onset Time (VOT) in English voiceless stops has been shown to be sensitive to place of articulation (Fischer-Jorgensen 1954), to contextual factors such as the height, tenseness, and duration of the following vowel and the voicing of coda consonants (Klatt 1975, Port & Rotunno 1979), to prosodic factors like stress and pitch (Lisker & Abramson 1967), and also to F0 (McCrea & Morris 2005) and speaking rate (Kessinger & Blumstein 1997, Allen 2003). We report two additional factors involving following consonants. We analyzed 120 /p/- and /k/-initial words produced by 148 Canadian English speakers (n = 17742). VOTs of the initial stops were measured semi-automatically and all other segment durations were measured using forced alignment. The results of a mixed-effects regression support earlier findings that VOT is longer in /k/, directly related to following vowel duration, inversely related to speech rate, longer before tense vowels, and shorter before voiceless codas. Additionally, we find that VOT is shorter when the next syllable starts with a phonetically voiceless plosive (i.e., excluding flapped /t/), and that the most relevant measure of vowel duration includes the duration of postvocalic liquids, even those that are typically analyzed as onsets.
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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 | 0.008 |
| 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) | 0.003 | 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 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".