Shorter Sentence Length Maximizes Intelligibility and Speech Motor Performance in Persons With Dysarthria Due to Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Purpose The purpose of this study was to investigate the effect of sentence length on intelligibility and measures of speech motor performance in persons with amyotrophic lateral sclerosis (ALS) and to determine how these effects were influenced by dysarthria severity levels. Method One hundred thirty-one persons with ALS were included in this study, stratified into 4 dysarthria severity groups. All participants produced sentences from 5 to 15 words in length. Intelligibility, speaking rate, and measures of speech pausing behavior (i.e., total speech duration, total pause duration, and mean speech event duration) were measured for each sentence. Linear mixed-effects models were used to determine the effect of sentence length on speech measures for speakers at different dysarthria severity levels. Results Results showed that speech intelligibility significantly declined at longer sentence lengths only for the speakers with ALS who had more advanced dysarthria symptoms; however, speakers with mild-to-severe dysarthria showed significant declines in speaking rate and speech pausing behavior at longer sentence lengths. Conclusions Findings suggest that producing shorter sentences may help maximize intelligibility for speakers with moderate-to-severe dysarthria secondary to ALS and may be a beneficial compensatory strategy for preserving motor effort for all speakers with dysarthria secondary to 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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".