PHONETIC SEGMENTATION OF EMOTIONAL SPEECH WITH HMM-BASED METHODS
Bibliographic record
Abstract
In the present work we address the problem of phonetic segmentation of emotional speech. Investigating various traditional and recent HMM-based methods for speech segmentation, which we elaborated for the specifics of emotional speech segmentation, we demonstrate that the HMM-based method with hybrid embedded-isolated training offers advantageous segmentation accuracy, when compared to other HMM-based models used so far. The increased precision of the segmentation is a consequence of the iterative training process employed in the hybrid-training method, which refines the model parameters and the estimated phonetic boundaries taking advantage of the estimations made at previous iterations. Furthermore, we demonstrate the benefits of using purposely-built models for each target category of emotional speech, when compared to the case of one common model built solely from neutral speech. This advantage, in terms of segmentation accuracy, justifies the effort for creating and employing the purposely-built segmentation models per emotion category, since it significantly improves the overall segmentation 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.003 |
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".