Author manuscript, published in "Interspeech 2013, Lyon: France (2013)" Speech planning as an index of speech motor control maturity
Bibliographic record
Abstract
This paper investigates speech motor control maturity in 4-year-old Canadian French children. Acoustic and ultrasound data recorded from four children, and for comparison, from four adults, are presented and analyzed. Maturity of speech motor control is assessed by measuring two characteristics: token-to-token variability of isolated vowels, as a measure of motor control accuracy, and extra-syllabic anticipatory coarticulation within V1-C-V2 sequences. In line with theories of optimal motor control, anticipatory coarticulation is assumed to be based on the use of internal models of the speech apparatus and its efficiency is considered to reflect the maturity of these representations. In agreement with former studies, token-to-token variability is larger in children than in adults. An anticipation of V2 in V1 was found in all adults but in none of the children studied so far. These results indicate that children's speech motor control is immature from two perspectives: insufficiently accurate motor control patterns for vowel production, and inability to anticipate forthcoming gestures. Both aspects are discussed and interpreted in the context of the immaturity of the internal representations of the speech motor apparatus in 4-year-old children. Index Terms: speech production development, speech motor control, co-articulation, planning. 1.
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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 | 0.003 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.210 | 0.056 |
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".