Nonintrusive speech quality estimation based on Perceptual Linear Prediction
Bibliographic record
Abstract
Objective measures of speech quality are attractive because they facilitate performance assessment of hearing aids (HAs) without the need for human listeners. Objective speech quality predictions are usually performed intrusively, wherein the “closeness” between the reference and HA output speech recordings is quantified. In this paper, we focus on nonintrusive estimation of HA speech quality based on Perceptual Linear Prediction (PLP) modeling approach. In PLP, perceptual phenomena such as non-uniform filter bank analysis and nonlinear mapping between sound intensity and its perceived loudness are incorporated into the linear prediction feature extraction process. In this work, PLP and PLP-based cepstral coefficients were computed from HA speech recordings and their statistical properties were utilized as features for speech quality estimation. A custom database of HA speech recordings obtained in different noisy and reverberant environments was used to investigate the predictive performance of these individual features. In addition, regression functions that linearly combined the features and mapped to the predicted quality scores, were derived and validated. Experimental results show that the proposed nonintrusive speech quality estimates correlate well with subjective ratings of speech quality by hearing impaired listeners.
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How this classification was reachedexpand
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 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".