Recognizing Human Emotion from Audiovisual Informaiton
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present an emotion recognition system to classify human emotional state from audiovisual signals. We extract prosodic, mel-frequency cepstral coefficient (MFCC), and formant frequency features to represent the audio characteristics of the emotional speech. A face detection scheme, based on the 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 a stepwise method based on Mahalanobis distance. A classification scheme involving the analysis of individual class and combinations of different classes is proposed. Our emotion recognition system is tested over a language and race independent database, and an overall recognition accuracy of 82.14% is achieved.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it