Speech emotion recognition on mobile devices based on modulation spectral feature pooling and deep neural networks
Bibliographic record
Abstract
In this study, the problem of speech emotion recognition (SER) in-the-wild is addressed. A new modulation spectral feature pooling scheme is proposed to mitigate the detrimental effects of background noise. On top of these features, two DNN-based architectures are tested for the prediction of arousal and valence emotional primitives: a multi-layer perceptron (MLP) and a recurrent neural network based on Long-Short Term Memory (LSTM). Experiments are conducted using the RECOLA dataset of spontaneous interactions. In order to simulate data collected in-the-wild, the clean speech files were corrupted with different levels of background noise and room impulse responses collected using a mobile device. Both stationary and non-stationary noise types (fan and babble) were considered in our experiments. Three distinct scenarios were explored: noise only, reverberation only and noise-plus-reverberation. Experimental results have shown that, in most of the scenarios, the proposed SER system achieved better performance in terms of concordance correlation coefficients (CCC) compared to the benchmark algorithm described in the 2016 Audio/Visual Emotion Challenge. The proposed feature system also showed to be more robust when noise-plus-reverberation is considered.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".