The voices of anger and disgust: Acoustic correlates in three languages
Bibliographic record
Abstract
Anger and disgust are believed to represent discrete human emotions with unique vocal signatures in spoken language. However, few investigations describe the acoustic dimensions of these vocal expressions, and some researchers have questioned whether disgust is robustly encoded in the vocal channel. This study sought to isolate acoustic parameters that differentiate utterances identified as sounding angry versus disgusted by listeners based on evidence from three separate languages: English, German, and Arabic. Two male and two female speakers of each language produced a list of pseudo-sentences (e.g., Suh fector egzullin tuh boshent) to convey a set of seven different emotions. The recordings were later judged by a group of native listeners to determine what emotional meaning was perceived from the prosodic features of each pseudo-utterance. Individual sentences identified systematically as conveying either anger or disgust (greater than 3× chance target recognition) were then analyzed acoustically for various parameters of fundamental frequency, amplitude, and duration. Analyses compared which acoustic parameter(s) were dominant for identifying anger versus disgust in each language, and whether these patterns appeared to vary across languages, with implications for understanding the specificity and universality of these emotion expressions in the vocal channel.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".