Tongue Movements and Their\nAcoustic Consequences in Amyotrophic\nLateral Sclerosis
Bibliographic record
Abstract
Objective: The relations between acoustic measures and their articulatory bases have rarely been tested in dysarthria but are important for diagnostic and treatment purposes. We tested the association between acoustic measures of F2 range and F2 slope with kinematic measures of tongue movement displacement and speed in individuals with amyotrophic lateral sclerosis (ALS) and healthy controls speaking at normal and slow rates. Relations between acoustic and kinematic measures and speech intelligibility were examined.\nResults: As healthy controls reduced their speaking rate, their F2 slopes and movement speeds decreased. In talkers with ALS, acoustic and kinematic variables were associated with changes in speaking rate, characteristic of disease progression. Participants with slow rate had shallower F2 slopes and slower movement speeds than those with normal rate. Relations between F2 range and tongue displacement were weaker. F2 slope, displacement, and duration were correlated with speech intelligibility most consistently.\nConclusion: Findings suggested that F2 slope is a useful marker for tracking disease progression in ALS. F2 slope reflects changes in tongue function with disease progression and is linked to speech intelligibility. Changes in movement speed, however, might be the earliest sign of disease in the tongue.
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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) | 0.001 | 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".