Creaky voice across language and gender: A study of Canadian English-French bilingual speech
Bibliographic record
Abstract
This study addresses how non-contrastive creaky voice varies among languages and across speakers (as a function of gender). Spontaneous speech from 9 English-French bilingual speakers born and raised in Ontario/Québec was collected from publicly available online data sources, amounting to roughly 5 min of speech per speaker-language pair and 13 992 vowels total. This corpus will reach 40 speakers by the conference. Acoustic analysis consisted of pitch tracking in Praat, providing a proportion of unreliable f0 tracks for each vowel as well as one spectral slope measure (H1*-H2*) and two Harmonics-to-Noise Ratios (CPP and HNR05) as acoustic correlates of creaky voice. Statistical significance was tested using mixed-effects regression models, with fixed effects of language, gender, and utterance position, and maximal by-word and by-speaker random effects. The main results for gender show that men's vowels have more unreliable f0 tracks, lower H1*-H2*, lower HNR05 and somewhat lower CPP, suggesting that male speakers are creakier overall. Regarding language, English displays more unreliable pitch tracking compared to French, providing some evidence for language-dependent vocal settings. Other acoustic correlates of creak, however, do not show consistent cross-linguistic 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".