On the relationship between vocal aesthetics and speech perception.
Bibliographic record
Abstract
This study reports data from four experiments exploring the interplay of meta-linguistic analyzes and lower level tasks with the goal of understanding how judgments of vocal aesthetics and voice typicality affect voice and phoneme processing. In the first, speakers of west coast North American English rated the attractiveness of 60 American English voices. Results from this experiment were compared against the following ones. In the second experiment listeners were asked to rate the typicality of each voice for its sex. Ratings for both showed a strong correlation, suggesting that vocal attractiveness and voice typicality are related. In the next experiment listeners were asked to quickly classify the voices as male or female. Faster reaction times correlated with judgments of higher typicality, but not with attractiveness. Finally a group of listeners were asked to classify the vowels produced by the voices. Here a correlation was found with both rating tasks such that listeners were faster at vowel classification for both the more attractive voices and the more typical ones. Moreover, a correlation was found between both online tasks such that voices that listeners classified quickly by sex were also classified quickly by vowel.
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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 | 0.011 |
| 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 | 0.001 | 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".