Metaheuristically Enabled System for Emotion Recognition using BiLSTM
Bibliographic record
Abstract
Emotion recognition from speech is vital for applications in human-computer interaction and mental health diagnostics. This paper presents an efficient approach to classify emotions using the Toronto Emotional Speech Set (TESS) dataset. Key audio features, including Mel-Frequency Cepstral Coefficients (MFCCs) and spectral characteristics, are extracted to represent the speech signals. To enhance computational efficiency and mitigate overfitting, metaheuristic optimization techniques, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are utilized for dimensionality reduction by selecting the most relevant features. These optimized features are then fed into a Bidirectional Long Short-Term Memory (BiLSTM) network, which effectively captures temporal dependencies in the speech data. The proposed system combines the strength of metaheuristic feature selection with the powerful learning capabilities of BiLSTM, achieving superior classification accuracy compared to traditional methods. Experimental results validate the efficacy of this hybrid approach, offering a robust and scalable solution for emotion recognition tasks. Keywords: Speech Emotion Recognition, TESS Dataset, Metaheuristic Optimization, Dimensionality Reduction, BiLSTM, Deep Learning
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".