Maintaining vowel distinctiveness in children with Steinert Myotonic Dystrophy: An ultrasound study
Bibliographic record
Abstract
Speech production entails appropriately timed contractions of many muscles. Steinert myotonic dystrophy, a neurodegenerative disease that causes muscle weakness and difficulties in muscle relaxation after muscle contraction, frequently affects orofacial articulatory dynamics leading to decreased speech intelligibility. We aimed to investigate the articulatory and acoustic characteristics of cardinal vowels produced by children with Steinert disease. We recruited fourteen 6- to 14-year-old French-speaking children diagnosed with Steinert disease and 14 aged-matched typically developing children. They were asked to produce repetitions of the vowels /i a u/ in consonant-vowel (CV) contexts. A synchronized ultrasound, Optotrak motion tracking system, and audio recording system was used to track lip and jaw displacement as well as tongue shape and position. Duration and formant values were also extracted. The Euclidean distance between vowels, in the formant space, was reduced in children with Steinert disease compared to the control children. Different patterns of articulatory contrasts were observed among the children, with some of them using more tongue contrasts than lip contrasts. Intelligibility tests conducted with adult listeners on a subset of the data show that some patterns are related to higher intelligibility scores than others.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".