The effects of different voice qualities on the perceived personality of a speaker
Bibliographic record
Abstract
Although previous studies investigated various aspects of voice quality perception and personality attribution there are no studies, to our knowledge, which simultaneously examine and compare the perception of various voice qualities when produced by the same individual. This work investigates how laryngeal and supralaryngeal voice quality variations of a speaker affect listeners' perceived personality traits (and thus perceived charisma) of that same speaker. Six Canadian English speakers produced paragraphs varying the following voice qualities: modal, creaky, breathy (natural and artificial), (hyper-)nasalization, and smiling (natural and extreme). Listeners of a perception experiment were then tasked to rate 10 statements for each presented audio stimulus. Statements were selected corresponding to a sub-section of the Big 5 personality traits shown to be linked to charisma perception. Results show significantly more positive listener ratings (i.e., higher ratings compared to modal) with medium effects sizes for both smiling variants across all personality traits. In contrast, creaky was perceived significantly more negatively overall for all personality traits, with a medium effect size. Nasal and breathy still achieved statistically significant rating differences compared to the modal baseline. However, the overall effect pattern was more complex, and effect sizes were small or negligible. Additionally, we found consistent differences for some voice qualities when examining listener ratings comparing male vs. female speakers: for both creaky and smiling (but not for other voice qualities), female speakers were rated more negatively when producing creaky for some personality traits, whereas both smiling variants were consistently rated higher for females compared to males.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".