Contributions of Voice and Nonverbal Communication to Perceived Masculinity–Femininity for Cisgender and Transgender Communicators
Bibliographic record
Abstract
Purpose The purpose of this study was twofold: (a) to identify a set of communication-based predictors (including both acoustic and gestural variables) of masculinity–femininity ratings and (b) to explore differences in ratings between audio and audiovisual presentation modes for transgender and cisgender communicators. Method The voices and gestures of a group of cisgender men and women ( n = 10 of each) and transgender women ( n = 20) communicators were recorded while they recounted the story of a cartoon using acoustic and motion capture recording systems. A total of 17 acoustic and gestural variables were measured from these recordings. A group of observers ( n = 20) rated each communicator's masculinity–femininity based on 30- to 45-s samples of the cartoon description presented in three modes: audio, visual, and audio visual. Visual and audiovisual stimuli contained point light displays standardized for size. Ratings were made using a direct magnitude estimation scale without modulus. Communication-based predictors of masculinity–femininity ratings were identified using multiple regression, and analysis of variance was used to determine the effect of presentation mode on perceptual ratings. Results Fundamental frequency, average vowel formant, and sound pressure level were identified as significant predictors of masculinity–femininity ratings for these communicators. Communicators were rated significantly more feminine in the audio than the audiovisual mode and unreliably in the visual-only mode. Conclusions Both study purposes were met. Results support continued emphasis on fundamental frequency and vocal tract resonance in voice and communication modification training with transgender individuals and provide evidence for the potential benefit of modifying sound pressure level, especially when a masculine presentation is desired.
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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 | 0.006 |
| 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 | 0.001 | 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".