A Comparison of an Artificial Intelligence Tool to Fundamental Frequency as an Outcome Measure in People Seeking a More Feminine Voice
Bibliographic record
Abstract
Objectives/Hypothesis An artificial intelligence (AI) tool was developed using audio clips of cis‐male and cis‐female voices based on spectral analysis to assess %probability of a voice being perceived as female (%Prob♀). This program was validated with 92% accuracy in cisgender speakers. The aim of the study was to assess the relationship of fo on %Prob♀ by a validated AI tool in a cohort of trans females who underwent intervention to feminize their voice with behavioral modification and/or surgery. Study Design Cohort study. Methods Fundamental frequency (fo) from prolonged vowel sounds (fo/a/) and fo from spontaneous speech (fo‐sp) were measured using the Kay Pentax Computerized Speech Lab (Montvale, NJ) in trans females postintervention. The same voice samples were analyzed by the AI tool for %Prob♀. Chi‐square analysis and regression models were performed accepting >50% Prob♀ as female voice. Results Forty‐two patients were available for analysis after intervention. fo‐sp post‐treatment was positively correlated with %Prob♀ (R = 0.645 [P < .001]). Chi‐square analysis showed a significant association between AI %Prob♀ >50% for the speech samples and fo‐sp >160 Hz (P < .01). Sixteen of 42 patients reached an fo‐sp >160 Hz. Of these, the AI program only perceived nine patients as female (>50 %Prob♀). Conclusion Patients with fo‐sp >160 Hz after feminization treatments are not necessarily perceived as having a high probability of being female by a validated AI tool. AI may represent a useful outcome measurement tool for patients undergoing gender affirming voice care. Level of Evidence 3 Laryngoscope, 131:2567–2571, 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".