Bibliographic record
Abstract
In this work, we investigate the recognition of human emotional states from audiovisual signals. We extract prosodic, Mel-frequency Cepstral Coeffieient (MFCC), and formant frequency features to represent the audio characteristic of the emotional speech. A face detection scheme based on HSV color model is used to detect the face from the background. The facial expressions are represented by Gabor wavelet features. We perform feature selection by using the stepwise method based on Mahalanobis distance. The selected features are used to classify the emotional data into their corresponding classes. Different classification algorithms including Gaussian Mixture Model (GMM), K-nearest neighbours(K-NN), Neural Network (NN), and Fisher's Linear Discriminant Analysis (FLDA) are compared in this study. An adaptive multi-classifier scheme involving the analysis of individual class and combinations of different classes is proposed. Our recognition system is tested over a language independent database. The proposed FLDA-based multi-classifier scheme achieves the best overall and individual class recognition accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".