Cross-Cultural Emotion Recognition and Comparison Using Convolutional Neural Networks
Bibliographic record
Abstract
The paper sets to define a comparison of emotions across 3 different cultures namely Canadian French, Italian, and North American. This was achieved using speech samples for each of the three languages subject to our study. The features used were MFCCs and were passed through convolutional neural network in order to verify their significance for the task of emotion recognition through speech. Three different systems were trained and tested, one for each language. The accuracy came to 71.10%, 79.07%, and 73.89% for each of them respectively. The aim was to prove that the feature vectors we used were representing each emotion well. A comparison across each emotion, gender and language was drawn at the end and it was observed that apart from the emotion neutral , every other emotion was expressed somewhat differently by each culture. Speech is one of the main vehicles to recognize emotions and is an attractive area to be studied with application to presenting and preserving different cultural and scientific heritage.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".