Phonetic correlates of vocal attractiveness in American English.
Bibliographic record
Abstract
This study explores fine-grained phonetic vocal characteristics that underpin vocal attractiveness. In general, while it is well known that F0 plays a major role in such judgments [see, e.g., Riding et al. (2006)] there is a distinct lack of more detailed examinations of the phenomenon [see Zuta (2007) for a notable exception]. Moreover, the term “attractiveness” is generally ill-defined and conflated with other terms (such as “pleasantness”). Therefore, the specific goal of this study is to replicate and extend such studies by including a large number of talkers, more detailed acoustic measures, and better definition the term “attractiveness”. Specifically, 60 talkers from California (30 female) produced isolated words controlled for phonetic content. These voices will be played to listeners who will judge the attractiveness of each talker. Ratings of these talkers will be compared against these acoustic measures: duration, average F0, F0 variation, spectral tilt, jitter, vowel space area, long term averaged spectrum, VOT, spectral mean of frication, and spectral peak of frication. The semantic value of “attractiveness” will be explored in follow-up questionnaires asking more detailed questions. Results will be compared against previous studies and will be discussed in terms of possible universal and culture-specific features.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".