Deep Learning for Voice Gender Identification: Proof‐of‐concept for <scp>Gender‐Affirming</scp> Voice Care
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The need for gender-affirming voice care has been increasing in the transgender population in the last decade. Currently, objective treatment outcome measurements are lacking to assess the success of these interventions. This study uses neural network models to predict binary gender from short audio samples of "male" and "female" voices. This preliminary work is a proof-of-concept for further work to develop an AI-assisted treatment outcome measure for gender-affirming voice care. STUDY DESIGN: Retrospective cohort study. METHODS: Two hundred seventy-eight voices from male and female speakers from the Perceptual Voice Qualities Database were used to train a deep neural network to classify voices as male or female. Each audio sample was mapped to the frequency domain using Mel spectrograms. To optimize model performance, we performed 10-fold cross validation of the entire dataset. The dataset was split into 80% training, 10% validation, and 10% test. RESULTS: Overall accuracy of 92% was obtained, both when considering the accuracy per spectrum and per patient metric. The accuracy of the model was higher for recognizing female voices (F1 score of 0.94) compared to male voices (F1 score of 0.87). CONCLUSIONS: This proof of concept study shows promising performance for further development of an AI-assisted tool to provide objective treatment outcome measurements for gender affirming voice care. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E1611-E1615, 2021.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".