What anticipatory coarticulation in children tells us about speech motor control maturity
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the role of motor control immaturity in the speech production characteristics of 4-year-old children, compared to adults. Specifically, two indices were examined: trial-to-trial variability, which is assumed to be linked to motor control accuracy, and anticipatory extra-syllabic vowel-to-vowel coarticulation, which is assumed to be linked to the comprehensiveness, maturity and efficiency of sensorimotor representations in the central nervous system. METHOD: Acoustic and articulatory (ultrasound) data were recorded for 20 children and 10 adults, all native speakers of Canadian French, during the production of isolated vowels and vowel-consonant-vowel (V1-C-V2) sequences. Trial-to-trial variability was measured in isolated vowels. Extra-syllabic anticipatory coarticulation was assessed in V1-C-V2 sequences by measuring the patterns of variability of V1 associated with variations in V2. Acoustic data were reported for all subjects and articulatory data, for a subset of 6 children and 2 adults. RESULTS: Trial-to-trial variability was significantly larger in children. Systematic and significant anticipation of V2 in V1 was always found in adults, but was rare in children. Significant anticipation was observed in children only when V1 was /a/, and only along the antero-posterior dimension, with a much smaller magnitude than in adults. A closer analysis of individual speakers revealed that some children showed adult-like anticipation along this dimension, whereas the majority did not. CONCLUSION: The larger trial-to-trial variability and the lack of anticipatory behavior in most children-two phenomena that have been observed in several non-speech motor tasks-support the hypothesis that motor control immaturity may explain a large part of the differences observed between speech production in adults and 4-year-old children, apart from other causes that may be linked with language development.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".