Speaking rate and language-specific voice onset time effects on burst amplitude: Cross-linguistic observations and implications for sound change
Bibliographic record
Abstract
The relationship between speaking rate and burst amplitude in voiceless plosives was investigated in two languages with differing oro-laryngeal timing implementations of phonological voicing, North American English and Indian Tamil. Burst amplitude (reflecting both intraoral pressure and flow geometry of the oral channel) was hypothesized to decrease in CV syllables with increasing speaking rate, which imposes temporal constraints on both intraoral pressure buildup behind the oral occlusion as well as respiratory air flow. Increased speaking rate led to decreased burst amplitude (relative to vowel amplitude) in both languages, with the magnitude of the effect being considerably weaker in Tamil, which has short-lag implementation of voice onset time. Bilabials in both languages were affected disproportionately relative to other places of articulation. Additionally, burst amplitudes were lower overall in Tamil, reflecting lower intraoral pressure which promotes faster vocalic onset. Results are discussed in terms of language-internaland extra-linguistic phonetic phenomena potentially serving as perceptual triggers for historical sound change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".