Return to Oz: Voice pitch facilitates assessments of men’s body size.
Bibliographic record
Abstract
Listeners associate low voice pitch (fundamental frequency and/or harmonics) and formants (vocal-tract resonances) with large body size. Although formants reliably predict size within sexes, pitch does not reliably predict size in groups of same-sex adults. Voice pitch has therefore long been hypothesized to confound within-sex size assessment. Here we performed a knockout test of this hypothesis using whispered and 3-formant sine-wave speech devoid of pitch. Listeners estimated the relative size of men with above-chance accuracy from voiced, whispered, and sine-wave speech. Critically, although men's pitch and physical height were unrelated, the accuracy of listeners' size assessments increased in the presence rather than absence of pitch. Size assessments based on relatively low pitch yielded particularly high accuracy (70%-80%). Results of Experiment 2 revealed that amplitude, noise, and signal degradation of unvoiced speech could not explain this effect; listeners readily perceived formant shifts in manipulated whispered speech. Rather, in Experiment 3, we show that the denser harmonic spectrum provided by low pitch allowed for better resolution of formants, aiding formant-based size assessment. These findings demonstrate that pitch does not confuse body size assessment as has been previously suggested, but instead facilitates accurate size assessment by providing a carrier signal for vocal-tract resonances.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".