Articulatory Movements During Vowels in Speakers With Dysarthria and Healthy Controls
Bibliographic record
Abstract
PURPOSE: This study compared movement characteristics of markers attached to the jaw, lower lip, tongue blade, and dorsum during production of selected English vowels by normal speakers and speakers with dysarthria due to amyotrophic lateral sclerosis (ALS) or Parkinson disease (PD). The study asked the following questions: (a) Are movement measures different for healthy controls and speakers with ALS or PD, and (b) Are articulatory profiles comparable for speakers with ALS and speakers with PD? METHOD: Nineteen healthy controls and 15 speakers with dysarthria participated in this study. The severity of dysarthria varied across individuals and between the 2 disorder groups. The stimuli were 10 words (i.e., seed, feed, big, dish, too, shoo, bad, cat, box, and dog) embedded into sentences read at a comfortable reading rate. Movement data were collected using the X-ray microbeam. Movement measures included distances, durations, and average speeds of vowel-related movement strokes. RESULTS: Differences were found (a) between speakers with ALS and healthy controls and (b) between speakers with ALS and PD, particularly in movement speed. Tongue movements in PD and ALS were more consistently different from healthy controls than jaw and lower lip movements. This study showed that the effects of neurologic disease on vowel production are often articulator-, vowel-, and context-specific. CONCLUSIONS: Differences in severity between the speakers with PD and ALS may have accounted for some of the differences in movement characteristics between the groups. These factors need to be carefully considered when describing the nature of speech disorder and developing empirically based evaluation and treatment strategies for dysarthria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".