Multiple Models Fusion for Multi-label Classification in Speech Emotion Recognition Systems
Why this work is in the frame
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Bibliographic record
Abstract
Emotions are the base to comprehend speech and respond to each other. However, interpreting emotions is not a simple process even for people, not to mention machines. Based on a ground truth investigation that we have done on the RML (Ryerson Multimedia Lab) dataset, we have found that the same emotional speech might be interpreted differently by a number of individuals despite it has only one label. In this study we provide a fresh perspective to the speech emotion recognition field where a system may identify the presence of one or more potential emotions per utterance. Giving the computer the potential of identifying numerous categories at the same time, will allow it to assess different scenarios when making a decision. In this paper, we introduce a robust Speech Emotion Recognition (SER) system for multi-label classification. The model was tested on the RML and it achieved 83.88% which outperforms state-of-the-art methods.
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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 | 0.001 |
| 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) | 0.000 | 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 it