Visual gender biases in English stop voicing perception
Bibliographic record
Abstract
Listeners leverage visual information, such as perceived speaker gender, during speech perception. While work has shown visual gender biases on fricative (Johnson & Strand, 1996) and vowel perception (Johnson et al., 1999), its effects on stop voicing perception are understudied. We present an identification task where visual gender primes (male/female portraits) were used to investigate perceived speaker gender effects on English stop voicing. Subjects (n = 22) identified ‘pa’/’ba’ syllables from an 11-step VOT continuum (0–50 ms) in 5 pitch levels (high: 250 Hz, 230 Hz, mid: 170 Hz, and low: 130 Hz, 110 Hz). High and low pitch tokens were always preceded by a female and male portrait, respectively. Mid pitch tokens were preceded by female or male images in separate blocks. Given that f0 is higher following voiceless obstruents (Lehiste & Peterson 1961; Mohr 1971), if listeners utilize visual gender cues, acoustically ambiguous tokens paired with a male portrait should elicit more voiceless "pa" responses than those paired with a female portrait. Preliminary results pattern in the expected direction; mid tokens are perceived as "pa" more when paired with a male (15–25 ms = 79% "pa" response) than a female face (73% "pa" response); however, this effect was not statistically significant.
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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 | 0.002 |
| 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".