Cross-linguistic universality in speech emotion recognition: Comparing multilingual and monolingual computational speech models
Bibliographic record
Abstract
Human listeners can recognize emotions from speech alone, even in unfamiliar languages (universality), while performing better in familiar languages even without lexical cues (in-group advantage). However, whether computational speech models exhibit these abilities remains unclear—let alone how multilingual training influences them, a question still understudied in human research as well. We explored these questions by comparing a multilingual Whisper model to its English-only counterpart across seven acted-speech corpora: three English and four non-English languages seen by the multilingual model (Canadian French, Japanese, Thai, Greek). Emotion classification results showed strong universality and weak in-group advantage compared to past human studies—the monolingual model’s high accuracy on non-English speech (78.95%) suggests strong universality, while its lower accuracy than the multilingual model (81.37%) reflects weak in-group advantage. Notably, the two models performed similarly on English (monolingual: 76.49%, multilingual: 77.73%), suggesting that multilingual exposure does not greatly improve emotion recognition even in familiar languages. Layer-wise probing showed performance remaining high across the network, indicating that the systems seem to represent emotion across layers. These findings suggest that large speech models partially mirror human abilities in emotional speech recognition and encode a surprisingly universal representation of emotion without language-specific fine-tuning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".