Do voices carry valid information about a speaker's personality?
Bibliographic record
Abstract
Research on links between peoples’ personality traits and their voices has primarily focused on other peoples’ personality judgments about a target person based on a target person’s vocal characteristics, particularly voice pitch. However, it remains unclear whether individual differences in voices are linked to actual individual differences in personality traits, and thus whether vocal characteristics are indeed valid cues to personality. Here, we investigate how the personality traits of the Five Factor Model of Personality, sociosexuality, and dominance are related to measured fundamental frequency (voice pitch) and formant frequencies (formant position). For this purpose, we conducted a secondary data analysis of a large sample (2,217 participants) from eleven different, independent datasets with a Bayesian approach. Results suggest substantial negative relationships between voice pitch and self-reported sociosexuality, dominance and extraversion in men and women. Thus, personality might at least partly be expressed in people’s voice pitch. Evidence for an association between formant frequencies and self-reported personality traits is not compelling but remains uncertain. We discuss potential underlying biological mechanisms of our effects and suggest a number of implications for future research.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".