Segment-Based Approach to the Recognition of Emotions in Speech
Why this work is in the frame
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Bibliographic record
Abstract
A new framework for the context and speaker independent recognition of emotions from voice, based on a richer and more natural representation of the speech signal, is proposed. The utterance is viewed as consisting of a series of voiced segments and not as a single object. The voiced segments are first identified and then described using statistical measures of spectral shape, intensity, and pitch contours, calculated at both the segment and the utterance level. Utterance classification is performed by combining the segment classification decisions using a fixed combination scheme. The performance of two learning algorithms, support vector machines and K nearest neighbors, is compared. The proposed approach yields an overall classification accuracy of 87% for 5 emotions, outperforming previous results on a similar database.
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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) | 0.000 | 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 it