Adaptive Feature Generation for Speech Emotion Recognition
Bibliographic record
Abstract
The issue of emotion recognition has received considerable critical attention in artificial intelligence and machine learning. In sentiment analysis fields, researchers recognize emotional states from speech, electroencephalograms, and images, etc. The speech signal is among the most widely used in emotion recognition. There are many speech features, including pitch, energy, linear prediction coefficients, mel-frequency cepstral coefficients, and the Teager energy operator. In this study, we explore the critical speech features for sentiment analysis. We modify our previous feature-generation method, which applies low-variance filtering and principal component analysis (for grouped features) to identify the features. We do not utilize between-class scatter here, but rather, the between class–scatter and within class–scatter ratio. Grouping is achieved according to the number of features—not correlation values. For an objective evaluation, we use the Ryerson Audio-Visual Database of Emotional Speech and Song, with a performance evaluation conducted in terms of classifier accuracy and computational complexity. Finally, we propose an effective feature-generation method to find the critical features for emotion recognition from speech.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".