A sociophonetic study of creaky voice across language, gender and age in Canadian English-French bilinguals
Bibliographic record
Abstract
This paper examines the acoustic correlates of creaky voice across language, gender and year of birth to investigate 1) the reliability of cross-linguistic differences in voice quality, 2) the direction and extent of gender differences with respect to creaky voice, and 3) the existence of an ongoing sound change targeting voice quality. Spontaneous speech from 49 Canadian English-French bilingual speakers was collected from publicly available online data sources. This corpus was processed and a range of acoustic measures of voice quality extracted using an automated pipeline with manual checks. Results do not show strong nor consistent evidence for cross-linguistic differences in creak. Regarding gender, men’s voices are unequivocally creakier, indicated by more unreliable f0 tracks, lower H1*–H2*, lower CPP and lower HNR < 500 Hz. As for age, results generally show more creak for older speakers, CPP and HNR < 500 Hz values increasing with YOB while other acoustic measures show no significant differences, suggesting that these effects are more likely due to vocal aging than sound change in progress. Contrary to popular perception and recent work claiming that young women are leaders in creaky voice use, this study finds that acoustic correlates of creak show the exact opposite: men’s voices are creakier and if anything, younger speakers are less creaky. Possible reasons for this discrepancy, reviewing recent perceptual work on creaky voice, are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 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".