Multimodal Emotion Recognition using Multi-Strategy Opposite Learning with Lyrebird Optimization Algorithm
Bibliographic record
Abstract
Emotion recognition is an essential aspect of human-computer interaction, particularly in the field of healthcare, human-computer interaction, teaching, and other fields. Human emotion features are used to recognize different types of emotions. However, Multimodal Emotion Recognition (MER) faces difficulties during data fusion, where misalignment or inconsistent quality across modalities reduce model accuracy. This research proposes Multi-Strategy Opposite Learning with Lyrebird Optimization Algorithm (MSOL-LOA) for modality analysis based on audio, video and emotional expressions. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDEES) is used to evaluate model performance, after which pre-processing is performed to reduce background noise and improve image quality for enhanced emotion recognition classification. Then, feature extraction is performed to identify and capture the most essential features of the human face for emotion detection. The performance of the LOA methods is evaluated based on the parameters of recall, precision, accuracy and F1-Score for effective emotion recognition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".