Vowel production in children with myotonic dystrophy: A lip-tube perturbation study
Bibliographic record
Abstract
Myotonic dystrophy, a neurodegenerative disease that causes muscle weakness and difficulties in muscle relaxation after contraction, frequently affects orofacial articulatory dynamics leading to decreased speech intelligibility, particularly in children. We aimed to investigate the effects of myotonic dystrophy on sensorimotor relationships in children’s speech through a study of compensations for a lip-tube perturbation. We recruited fourteen 6- to 14-year-old French-speaking children diagnosed with myotonic dystrophy and 14 aged-matched typically developing children. They were asked to produce repetitions of the vowel /u/ with and without a 15-mm-diameter tube inserted between the lips. 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. Separate analyses were conducted on the first (F1), the second formant (F2), and the fundamental frequency (F0). Results revealed a significant main effect of group and an interaction between group and condition. Perceptual ratings of the produced vowels suggest that children with myotonic dystrophy rely more on auditory feedback than their typically developing peers. Together, results suggest that auditory feedback plays an important role in speech compensation, especially when the production system is impaired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".