Objective and Subjective Speech Quality Assessment of Amplification Devices for Patients With Parkinson’s Disease
Bibliographic record
Abstract
This paper investigated subjective and objective assessment of Parkinsonian speech quality. Speech stimuli were recorded from 11 Parkinsonian and 10 age-matched normal control participants under different amplification and environmental conditions. Quality ratings of the recorded stimuli were obtained from naïve listeners. For objective assessment, feature vectors were derived from the speech recordings based on temporal, spectral, and/or cepstral parametrization. These feature vectors were subsequently mapped to the predicted quality scores through several regression methods, including support vector regression, Gaussian process regression, and deep learning. Analyses of subjective speech quality ratings showed that Parkinsonian speech quality was significantly poorer than control subjects' speech quality, and that the amplification devices differentially affected perceived quality of Parkinsonian speech. Objective analyses revealed disparity in performance among feature vectors and mappers, with some feature vector and mapper combinations exhibiting statistically similar correlations with subjective ratings. A set consisting of cepstral, spectral, and modulation domain speech features when combined with Gaussian process regression or deep learning resulted in the highest correlation of 0.85 with the subjective data.
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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 | 0.000 |
| 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 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".