Creaky voice perception modulated by f0 and gender cues
Bibliographic record
Abstract
Creaky voice has traditionally been associated with men's speech (e.g., Henton & Bladon, 1988), with acoustic work corroborating this long-standing claim (e.g., Gittelson et al., 2021; Klatt & Klatt, 1990). Since around 2010, public discourse and well-cited sociolinguistic work (e.g., Podesva, 2013; Yuasa, 2010) have perpetuated increased creak use by women, typically implementing perceptual coding. This study investigates this mismatch by testing how creaky voice perception is modulated by (perceived) speaker gender and f0. Using a matched-guise paradigm, 40 Canadian English listeners rated modal and creaky voices—altered to have ambiguously gendered formants and median f0 (115,135, 155 Hz), and paired with female and male faces—for perceived creakiness along a visual-analog scale. Bayesian regression models showed robust effects of voice quality and moderate effects of f0: creaky and lower f0 stimuli rated as creakier. We found no evidence of female faces increasing creakiness ratings overall. However, a weak interaction between face gender and f0 suggests a possible gender prototypicality effect: creakiness ratings slightly higher for female faces at low f0 and for male faces at higher f0. These results indicate that reports of increased creak in women’s voices cannot be explained by social gender bias or acoustic bias alone.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".