Consonant Acoustics in Parkinson's Disease and Multiple Sclerosis: Comparison of Clear and Loud Speaking Conditions
Bibliographic record
Abstract
PURPOSE: The impact of clear speech or an increased vocal intensity on consonant spectra was investigated for speakers with mild dysarthria secondary to multiple sclerosis or Parkinson's disease and healthy controls. METHOD: Sentences were read in habitual, clear, and loud conditions. Spectral moment coefficients were obtained for word-initial and word-medial /s/, /ʃ/, /t/, and /k/. Global production differences among conditions were confirmed with measures of vocal intensity and articulation rate. RESULTS: Static or slice-in-time first moments (M1) for loud differed most frequently from habitual, but neither loud nor clear enhanced M1 contrast for consonant pairs. In several instances, the clear and loud conditions yielded stable or nonvarying fricative M1 time histories. Spectral contrast was reduced for word-medial versus word-initial consonant pairs. CONCLUSION: The finding that the loud and especially clear condition yielded fairly subtle changes in consonant spectra suggests these global techniques may minimally enhance consonant segmental production or contrast in mild dysarthria. The robust effect of word position on consonant spectra indicates that this variable deserves consideration in future studies. Future research also is needed to investigate how or whether consonant production bears on the improved intelligibility previously reported for these global dysarthria treatment techniques.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".