Bayesian Model Based Non-Intrusive Speech Quality Evaluation
Why this work is in the frame
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Bibliographic record
Abstract
A novel Bayesian model-based non-intrusive speech quality evaluation (BM-NiSQE) algorithm is presented in this paper. The proposed BM-NiSQE algorithm employs a statistical model approach and Bayesian inference to estimate the speech quality only using the output signal of the system under test. In the proposed algorithm, the speech features are extracted by perceptual spectral analysis. Gaussian mixture density hidden Markov models (GMD-HMMs) are exploited to characterize different speech quality categories, which take into account not only the temporal variations of speech signal but also the spectral statistical characteristics in the perception domain. Based on the trained GMD-HMMs, the prediction of speech quality is carried out by Bayesian inference and minimum mean square error (MMSE) estimation. Preliminary experimental results show that the predicted results of the proposed algorithm correlate well with the subjective quality scores.
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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 | 0.001 | 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