Reliable but weak voice-formant cues to body size in men but not women
Bibliographic record
Abstract
Whether voice formants provide reliable cues to adult body size has been contested recently for some animals and humans and the outcome bears critically on theories of social competition and mate choice, language origins, and speaker normalization. We report two experiments to test listeners’ ability to assess speaker body size. In Experiment 1, listeners heard paired comparisons of the same short phrase spoken by two adults of the same sex paired randomly with respect to height and indicated which was larger. Both sexes (M=20; F=22) showed an equal but modest ability to identify the larger male (mean correct=58.5%; T=31.5, P<0.001) that correlated with the magnitude of their height difference but could not pick the larger female (mean correct=52.0%; T=1.05, P=0.305) regardless of the height difference. Experiment 2 used single word comparisons, focused only on male voices, and controlled F0 while manipulating F1−F4 between speakers. When F0 was equal but F1−F4 predicted the height difference between speakers, both sexes (M=12; F=18) correctly chose the taller male (80%). When F1−F4 values of the shorter male were reduced below those of the taller male (or vice versa), subjects shifted to pick the shorter male as being larger.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".