A Hybrid Approach of Long Short Term Memory and Transformer Models for Speech Emotion Recognition
Bibliographic record
Abstract
Speech emotion recognition (SER) has become a critical component of the next generations of technologies that interact between humans and machines. However, in this paper, we explore the advantage of the hybrid LSTM + Transformer model over the solo LSTM and Transformer models. The proposed method contains the following steps: data loading using benchmark datasets such as the Toronto Emotional Speech Set (TESS), Berlin Emotional Speech Database (EMO-DB), and (SAVEE). Secondly, to create a meaningful representation to preprocess raw audio data, Mel-Frequency Cepstral Coefficients (MFCCs) are used; thirdly, the model’s architecture is designed and explained. Finally, we evaluate the precision, recall, F1 score, classification reports, and confusion matrices of the model. The outcome of this experiment based on classification reports and confusion matrices shows that the hybrid LSTM + Transformer model has a remarkable performance on the TESS-DB, surpassing the other models with a 99.64% accuracy rate, while the LSTM model gained 97.50% and the Transformer model achieved 98.21%. For the EMO-DB, the LSTM model achieved the highest accuracy of 73.83%, followed by the hybrid that gained 71.96%, and the Transformer model achieved 70.09%. Lastly, LSTM obtained the highest performance on SAVEE-DB of 65.62% accuracy, followed by the Transformer model which achieved 58.33%, and the hybrid model achieved 56.25%.
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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 | 0.000 | 0.002 |
| 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".