Token-to-token variability and anticipatory coarticulation as indicators of maturity of speech motor control in 4-year-old children
Bibliographic record
Abstract
Children's gestures do not appear to be executed with the same dexterity as adults'. Studies of arm movements have shown that young children's gestures are less accurate, more variable and slower than those of adults. This difference in behavior can be explained by a lack of experience with the sensory consequences of motor acts and still-developing forward models for the control of those acts. The hypothesis of immature and incomplete sensori-motor representations for speech in 4-year-old native speakers of Canadian French is addressed here through the analysis of ultrasound recordings of tongue contour kinematics and the speech signal from a corpus of isolated vowels and vowel-consonant-vowel sequences. Special attention is devoted to the analysis of vowel variability from two perspectives. Variability across repetitions in a single context provides information about the accuracy of the control. Contextual variability provides insights into the planning process as reflected in anticipatory coarticulation. Analysis of the observed lingual gestures will lead to improved understanding of the development of speech motor control and refinement of sensori-motor representations of speech. [Work supported by FQRNT project N 147877 and ANR project ANR-08-BLAN-0272]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".