Cultural differences in vocal expression analysis: Effects of task, language, and stimulus-related factors
Bibliographic record
Abstract
Cultural context shapes the way that emotions are expressed and socially interpreted. Building on previous research looking at cultural differences in judgements of facial expressions, we examined how listeners recognize speech-embedded emotional expressions and make inferences about a speaker's feelings in relation to their vocal display. Canadian and Chinese participants categorized vocal expressions of emotions (anger, fear, happiness, sadness) expressed at different intensity levels in three languages (English, Mandarin, Hindi). In two additional tasks, participants rated the intensity of each emotional expression and the intensity of the speaker's feelings from the same stimuli. Each group was more accurate at recognizing emotions produced in their native language (in-group advantage). However, Canadian and Chinese participants both judged the speaker's feelings to be equivalent or more intense than their actual display (especially for highly aroused, negative emotions), suggesting that similar inference rules were applied to vocal expressions by the two cultures in this task. Our results provide new insights on how people categorize and interpret speech-embedded vocal expressions versus facial expressions and what cultural factors are at play.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".