Interactions between speech coders and disordered speech
Bibliographic record
Abstract
We examined the impact of standard speech coders currently used in modern communication systems, on the quality of speech from persons with common speech and voice disorders. Four standardized coders, viz. G. 728 LD-CELP, GSM 6.10 RPE-LTP, FS1016 CELP, FS1015 LPC and the recently proposed US Federal Standard 2400 bps MELP were evaluated with speech samples collected from 30 disordered talkers. Objective speech quality measures, including the auditory distance parameter based on the measuring normalizing blocks technique, and the perceptual speech quality measure, and subjective impressions of speech coder performance were used to assess the interaction between speech coder and speech disorder. Objective speech quality measures revealed that the performance of the LD-CELP and GSM RPE-LTP coders was not measurably influenced by the type of input speech, and that MELP, FS1015 LPC and to a certain extent FS1016 CELP exhibited degraded performance with speech samples from disordered talkers. Results from perceptual experiments were in contrast with the objective measures of speech quality; ratings of speech coder performance indicated that the listeners are less sensitive to coder-induced distortions with abnormal speech samples.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".