Hybrid-Module Transformer: enhancing speech emotion recognition with HuBERT, LSTM, and ResNet-50
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Speech emotion recognition (SER) is a challenging task that involves identifying human emotions from speech. Traditional sequence models like recurrent neural network (RNN) and long short-term memory (LSTM) are limited by vanishing gradients and difficulty in capturing long-range dependencies. This article presents a novel model based on the Hybrid-Module-Transformer, which leverages the capabilities of Transformer modules to extract feature representations effectively, even with limited data. The model combines the strengths of Hidden-Unit BERT (HuBERT), LSTM, and Residual Network (ResNet-50) to achieve superior performance in speech emotion classification tasks. In the model, we utilized Mel-frequency cepstral coefficients (MFCC) and Spectrogram for feature extraction. Then, a HuBERT-LSTM framework is used to perform both speech-to-text recognition and emotion classification. We evaluate the model on two benchmark datasets: Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Multimodal EmotionLines Dataset (MELD). On the RAVDESS dataset, the model achieves a maximum accuracy of 76% and precision of 78%, while on the more challenging MELD dataset, it attains an accuracy of 72.9% and precision of 72.3%. These results demonstrate the effectiveness and generalizability of our model in both controlled and real-world conversational scenarios, making it a competitive solution for robust speech emotion recognition.
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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