Similarities in the acoustic expression of emotions in English, German, Hindi, and Arabic.
Bibliographic record
Abstract
Based on the hypothesis that emotion expression is in large part biologically determined (“universal”), this study examined whether spoken utterances conveying seven emotions (anger, disgust, fear, sadness, happiness, surprise, and neutral) demonstrate similar acoustic patterns in four distinct languages (English, German, Hindi, and Arabic). Emotional pseudoutterances (the dirms are in the cindabal) were recorded by four native speakers of each language using an elicitation paradigm. Across languages, approximately 2500 utterances, which were perceptually identified as communicating the intended target emotion, were analyzed for three acoustic parameters: f0Mean, f0Range, and speaking rate. Combined variance in the three acoustic measures contributed significantly to differences among the seven emotions in each language, although f0Mean played the largest role for each language. Disgust, sadness, and neutral were always produced with a low f0Mean, whereas surprise (and usually fear and anger) exhibited an elevated f0Mean. Surprise displayed an extremely wide f0Range and disgust exhibited a much slower speaking rate than the other emotions in each language. Overall, the acoustic measures demonstrated many similarities among languages consistent with the notion of universal patterns of vocal emotion expression, although certain emotions were poorly predicted by the three acoustic measures and probably rely on additional acoustic parameters for perceptual recognition.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".