Pretreatment Acoustic Predictors of Gender, Femininity, and Naturalness Ratings in Individuals With Male-to-Female Gender Identity
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the pretreatment acoustic characteristics of individuals with male-to-female gender identity (IMtFGI) and investigate the ability of the acoustic measures to predict ratings of gender, femininity, and vocal naturalness. METHOD: This retrospective descriptive study included 2 groups of participants. Speakers were IMtFGI who had not previously received communication feminization treatment (N = 25). Listeners were members of the lay community (N = 30). Acoustic data were retrospectively obtained from pretreatment recordings, and pretreatment recordings also served as stimuli for 3 perceptual rating tasks (completed by listeners). RESULTS: Acoustic data generally were within normal limits for male speakers. All but 2 speakers were perceived to be male, limiting information about the relationship between acoustic measures and gender perception. Fundamental frequency (reading) significantly predicted femininity ratings (p = .000). A total of 3 stepwise regression models indicated that minimum frequency (range task), second vowel formant (sustained vowel), and shimmer percentage (sustained vowel) together significantly predicted naturalness ratings (p = .005, p = .003, and p = .002, respectively). CONCLUSIONS: Study aims were achieved with the exception of acoustic predictors of gender perception, which could be described for only 2 speakers. Future research should investigate measures of prosody, voice quality, and other aspects of communication as predictors of gender, femininity, and naturalness.
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 | 0.000 |
| Scholarly communication | 0.000 | 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".