Acoustic correlates of comorbid voice and resonance impairment in individuals with amyotrophic lateral sclerosis
Bibliographic record
Abstract
Assessment of voice and resonance impairment in amyotrophic lateral sclerosis (ALS) may be challenging due to multi-speech subsystem involvement. Although several acoustic measures have been associated with isolated voice and resonance impairment, their efficacy in the presence of comorbid voice-resonance impairment is unclear. The goal of this work is to determine acoustic features that correlate with perceptual judgment of voice and resonance severity in patients with ALS, and identify measures capable of differentiating phonatory, resonance, and co-occurring impairments. Two listeners rated resonance and voice impairment severity of repetitions of “Buy Bobby a puppy” produced by 26 participants with ALS. Samples were stratified based on perceptual ratings: bulbar asymptomatic, predominantly phonatory involvement (i.e., abnormal voice), predominantly resonatory involvement (hypernasality), and mixed (phonatory and resonance involvement). Groups were compared using resonance (one-third octave analysis) and phonatory (cepstral/spectral) measures. The one-third octave analysis differentiated all groups (p < 0.05); the cepstral peak prominence differentiated all groups (p < 0.01) except asymptomatic versus mixed; and the low/high spectral ratio did not differ between groups. Findings illustrate the challenges of implementing targeted resonance and voice measures in the presence of multi-speech system involvement, though one-third octave analysis is a promising approach to quantifying voice and resonance impairment in ALS.
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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 | 0.003 |
| 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 | 0.001 |
| 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".