Co-Occurrence of Hypernasality and Voice Impairment in Amyotrophic Lateral Sclerosis: Acoustic Quantification
Bibliographic record
Abstract
PURPOSE: Hypernasality and atypical voice characteristics are common features of dysarthric speech due to amyotrophic lateral sclerosis (ALS). Existing acoustic measures have been developed to primarily target either hypernasality or voice impairment, and the effects of co-occurring hypernasality-voice problems on these measures are unknown. This report explores (a) the extent to which acoustic measures are affected by concurrent perceptually identified hypernasality and voice impairment due to ALS and (b) candidate acoustic measures of early indicators of hypernasality and voice impairment in the presence of multisystem involvement in individuals with ALS. METHOD: = 27). The samples were stratified based on perceptual ratings: voice/hypernasality asymptomatic, predominantly hypernasal, predominantly voice impairment, and mixed (co-occurring hypernasality and voice impairment). Groups were compared using established acoustic measures of hypernasality (one-third octave analysis) and voice (cepstral/spectral analysis) impairment. RESULTS: The one-third octave analysis differentiated all groups; the cepstral peak prominence differentiated all groups except asymptomatic versus mixed, whereas the low-to-high spectral ratio did not differ among groups. Additionally, one-third octave analyses demonstrated promising speech diagnostic potential. CONCLUSIONS: The results highlight the need to consider the validity of measures in the context of multisubsystem involvement. Our preliminary findings further suggest that the one-third octave analysis may be an optimal approach to quantify hypernasality and voice abnormalities in the presence of multisystem speech impairment. Future evaluation of the diagnostic accuracy of the one-third octave analysis is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".