Multimodal Speech Emotion Recognition via Transformer-Based Hybrid Fusion and Dual Cross-entropy Techniques
Bibliographic record
Abstract
Speech emotion recognition is gaining increasing interest in the academic sphere due to the advancement of machine intelligence in the service industries. The previous research has already validated the efficacy of multimodality in Speech Emotion Recognition (SER); yet most studies have focused on one-time fusion techniques. This paper proposes a hybrid fusion architecture which optimizes the advantages of multiple fusion techniques and modalities. The model is predominantly based on the rapidly rising Transformer architecture. This study also extends the classic cross-entropy loss and designs a novel loss function which differentiates the misprediction patterns. The architecture is experimented on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset with sufficient cross-validation. It reaches 89.7% accuracy and outmatches the State-of-the-art (SOTA) methods. The performance is further enhanced by the proposed loss function and arrives at 91.1% accuracy. In addition, the models show computation scalability and few needs for hyperparameter fine-tuning. This article concludes that more comprehensive fusion techniques are worth exploration for multimodal speech emotion recognition and Transformers are suitable for emotional characteristics and lead the classification process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".