Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set
Bibliographic record
Abstract
Speech Emotion Recognition (SER) is a popular topic in academia and industry. Feature engineering plays a pivotal role in building an efficient SER. Although researchers have done a tremendous amount of work in this field, there are still the issues of speech feature choice and the correct application of feature engineering that remains to be solved in the domain of SER. In this research, a feature optimization approach that uses a clustering-based genetic algorithm is proposed. Instead of randomly selecting the new generation, clustering is applied at the fitness evaluation level to detect outliers for exclusion to be part of the next generation. The approach is compared with the standard Genetic Algorithm in the context of audio emotion recognition using Berlin Emotional Speech Database (EMO-DB), Ryerson Audio-Visual Database of Speech and Song (RAVDESS) and, Surrey Audio-Visual Expressed Emotion Dataset (SAVEE). Results signify that the proposed technique effectively improved the emotion classification in speech. The recognition rate of 89.6% for general speakers (both male and female), 86.2% for male speakers, and 88.3% for female speakers on EMO-DB, 82.5% for general speakers, 75.4% for male speakers, and 91.1% for female speaker on RAVDESS, and 77.7% for general speakers on SAVEE is obtained in speaker-dependent experiments. For speaker-independent experiments, we achieved the recognition rate of 77.5% on EMO-DB, 76.2% on RAVDESS and, 69.8 % on SAVEE. All the experiments were performed on MATLAB and the Support Vector Machine (SVM) was used for classification. Results confirm that the proposed method is capable of discriminating emotions effectively and performed better than the other approaches used for comparison in terms of performance measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".