Conditional Transformer-Based U-Net Architecture for Speech Emotion Recognition
Bibliographic record
Abstract
In this paper, we introduce a conditional transformer-based U-net architecture for speech emotion recognition (SER). The proposed architecture consists of convolutional transformer (CTR)-based encoder, feature decoder and emotion decoder. The CTR-based U-net encoder is designed to extract compressed bottleneck features for recognition. The emotion is then predicted by the CTR-based emotion decoder, where we use additional trainable emotion query to better capture the characteristics of different emotional states. The CTR-based feature decoder reconstructs the given input features from the bottleneck features. This auxiliary decoder allows us to use additional information while training the model, which further improves the recognition performance. Specifically, the proposed feature decoder is performed through sophisticated embedding of the predicted emotional states and the features of the encoder layers. In the proposed framework, we consider the channel attention for each time frame in the CTRs to better enable a real-time processing of emotion recognition. Experimental results showed that the proposed emotion classification method provided better recognition performance than the selected benchmark algorithms.
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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 | 0.000 |
| 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 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".