Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Automatic speech emotion recognition (SER) has gained popularity over the last decade and numerous Challenges have emerged. While the latest Challenges have shown that deep neural networks achieve the best results, existing input features are still a bottleneck and cause severe performance degradation in realistic “in-the-wild” scenarios. In this paper, we propose two innovations to tackle this issue. First, we propose to combine the bag-of-audio-words methodology with modulation spectrum features for environmental robustness. Second, we take advantage of the inherent quality-awareness properties of modulation spectrum and propose the use of a quality feature as an additional feature to be used by the speech emotion recognizer. Experiments are conducted with three multi-lingual speech datasets used in recent SER Challenges degraded by different noise sources and levels, and room reverberation. Experimental results show the proposed features i) consistently outperforming benchmark systems, ii) providing complementary information to classical features, hence improving performance with feature fusion, and iii) showing robustness against environment and language mismatch. Moreover, we show that when the proposed system is provided with quality information, further improvements are obtained. Overall, the proposed bag of modulation spectrum features are shown to be a promising candidate for “in-the-wild” SER.
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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 | 0.001 | 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