Feature Pooling of Modulation Spectrum Features for Improved Speech Emotion Recognition in the Wild
Bibliographic record
Abstract
Interest in affective computing is burgeoning, in great part due to its role in emerging affective human-computer interfaces (HCI). To date, the majority of existing research on automated emotion analysis has relied on data collected in controlled environments. With the rise of HCI applications on mobile devices, however, so-called “in-the-wild” settings have posed a serious threat for emotion recognition systems, particularly those based on voice. In this case, environmental factors such as ambient noise and reverberation severely hamper system performance. In this paper, we quantify the detrimental effects that the environment has on emotion recognition and explore the benefits achievable with speech enhancement. Moreover, we propose a modulation spectral feature pooling scheme that is shown to outperform a state-of-the-art benchmark system for environment-robust prediction of spontaneous arousal and valence emotional primitives. Experiments on an environment-corrupted version of the RECOLA dataset of spontaneous interactions show the proposed feature pooling scheme, combined with speech enhancement, outperforming the benchmark across different noise-only, reverberation-only and noise-plus-reverberation conditions. Additional tests with the SEWA database show the benefits of the proposed method for in-the-wild applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".