Robust speech emotion recognition using conditional transformer-based architecture
Bibliographic record
Abstract
Robustness remains a fundamental challenge in real-world speech recognition, particularly under adverse acoustic conditions. In this paper, we introduce a robust speech emotion recognition (SER) approach using a conditional transformer-based architecture, specifically designed to maintain consistent performance across a wide range of noisy acoustic environments. The proposed convolutional transformer (CTr)-based architecture consists of three primary modules: a noise-classification module (NCM), a speech feature enhancement module (SFEM) and a speech emotion recognition module (SERM). The NCM first identifies the noise type presented in the given noisy speech features, producing embeddings that condition both the SFEM and SERM to better capture the characteristics of different noise types. The front-end SFEM enhances noisy speech features to improve the robustness of the SERM in adverse acoustic conditions and finally, the SERM predicts the emotional states. Experimental results show that that the proposed architecture performs robust under various noisy acoustic environment and provide better results than the selected benchmark methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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; both teacher heads agree on what is shown here.
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".