An investigation of speech-based human emotion recognition
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents our recent work on recognizing human emotion from the speech signal. The proposed recognition system was tested over a language, speaker, and context independent emotional speech database. Prosodic, Mel-frequency cepstral coefficient (MFCC), and formant frequency features are extracted from the speech utterances. We perform feature selection by using the stepwise method based on Mahalanobis distance. The selected features are used to classify the speeches into their corresponding emotional classes. Different classification algorithms including maximum likelihood classifier (MLC), Gaussian mixture model (GMM), neural network (NN), K-nearest neighbors (K-NN), and Fisher's linear discriminant analysis (FLDA) are compared in this study. The recognition results show that FLDA gives the best recognition accuracy by using the selected features.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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