Hybrid Differential Evolution and Particle Swarm Optimization for Speech Emotion Classification
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel evolutionary-based hybridization approach for speech emotion classification. This approach is based on two optimization algorithms: differential evolution (DE) and particle swarm optimization (PSO), using three types of acoustics features to represent and explore a ‘tri-population’ environment. Mel-frequency cepstral coefficients (MFCCs), prosodic features, and auditory-based parameters are considered to compose each population, respectively. The hybridization of PSO and DE methods, as well as the relevance of acoustic representation techniques, are investigated to provide the most effective configuration for speech emotion classification. Evaluation experiments are carried out using the Emotional Prosody Speech and Transcripts, and the support vector machine (SVM) as an objective function. The results showed that the PSO-DE-PSO hybrid method achieves the best performance.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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