Spectral analysis of strident fricatives in cisgender and transfeminine speakers
Bibliographic record
Abstract
The spectral features of /s/ and /ʃ/ carry important sociophonetic information regarding a speaker's gender. Often, gender is misclassified as a binary of male or female, but this excludes people who may identify as transgender or nonbinary. In this study, we use a more expansive definition of gender to investigate the acoustics (duration and spectral moments) of /s/ and /ʃ/ across cisgender men, cisgender women, and transfeminine speakers in voiced and whispered speech and the relationship between spectral measures and transfeminine gender expression. We examined /s/ and /ʃ/ productions in words from 35 speakers (11 cisgender men, 17 cisgender women, 7 transfeminine speakers) and 34 speakers (11 cisgender men, 15 cisgender women, 8 transfeminine speakers), respectively. In general, /s/ and /ʃ/ center of gravity was highest in productions by cisgender women, followed by transfeminine speakers, and then cisgender men speakers. There were no other gender-related differences. Within transfeminine speakers, /s/ and /ʃ/ center of gravity and skewness were not related to the time proportion expressing their feminine spectrum gender or their Trans Women Voice Questionnaire scores. Taken together, the acoustics of /s/ and /ʃ/ may signal gender group identification but may not account for within-gender variation in transfeminine gender expression.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".