Individual Talker and Token Covariation in the Production of Multiple Cues to Stop Voicing
Bibliographic record
Abstract
BACKGROUND/AIMS: Previous research found that individual talkers have consistent differences in the production of segments impacting the perception of their speech by others. Speakers also produce multiple acoustic-phonetic cues to phonological contrasts. Less is known about how multiple cues covary within a phonetic category and across talkers. We examined differences in individual talkers across cues and whether token-by-token variability is a result of intrinsic factors or speaking style by examining within-category correlations. METHODS: We examined correlations for 3 cues (voice onset time, VOT, talker-relative onset fundamental frequency, f0, and talker-relative following vowel duration) to word-initial labial stop voicing in English. RESULTS: VOT for /b/ and /p/ productions and onset f0 for /b/ productions varied significantly by talker. Token-by-token within-category variation was largely limited to speaking rate effects. VOT and f0 were negatively correlated within category for /b/ productions after controlling for speaking rate and talker mean f0, but in the opposite direction expected for an intrinsic effect. Within-category talker means were correlated across VOT and vowel duration for /p/ productions. Some talkers produced more prototypical values than others, indicating systematic talker differences. CONCLUSION: Relationships between cues are mediated more by categories and talkers than by intrinsic physiological relationships.Talker differences reflect systematic speaking style differences.
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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 | 0.004 |
| 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".