Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning
Bibliographic record
Abstract
Speech-based emotion recognition is still challenging due to its complexity despite being widely used in applications relating to emotions.In this paper, we developed a framework by considering three features: Prosodic features, Wavelet, and Spectral features.Under Prosodic, pitch and energy are considered, while under wavelet features, the approximation and detailed sub-bands ate fur scales are considered.Mel-Frequency Cepstral Coefficients (MFCC), Formants, and Long-Term Average Spectrum (LTAS) are all measured from speech signals as part of spectral features.Further, the significant features are selected based on nonlinear statistics, and dimensionality reduction is accomplished through Fisher Criterion.Spearman Rank Correlation is employed to find the nonlinear statistics under correlation analysis.For categorization, a Support Vector Machine and Decision Tree are used.The proposed method is simulated over RAVDESS, SAVEE, EMOVO, and URDU databases, and the observed recognition rates are approximately 79.66%, 88.99%, 87.68%, and 95.78%, respectively.
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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 | 0.000 | 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) | 0.002 | 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".