Role of Vocal Tract Morphology in Speech Development
Bibliographic record
Abstract
The development of speech from infancy to adulthood results from the interaction of neurocognitive factors, by which phonological representations and motor control abilities are gradually acquired, and physical factors, involving the complex changes in the morphology of the articulatory system. In this article, an articulatory-to-acoustic model, integrating nonuniform vocal tract growth, is used to describe the effect of morphology in the acoustic and perceptual domains. While simulating mature control abilities of the articulators (freezing neurocognitive factors), the size and shape of the vocal apparatus are varied, to represent typical values of speakers from birth to adulthood. The results show that anatomy does not prevent even the youngest speaker from producing vowels perceived as the 10 French oral vowels /i y u e phi o epsilon oe [symbol: see text] a/. However, the specific configuration of the vocal tract for the newborn seems to favor the production of those vowels perceived as low and front. An examination of the acoustic effects of articulatory variation for different growth stages led to the proposed variable sensorimotor maps for newbornlike, childlike, and adultlike vocal tracts. These maps could be used by transcribers of infant speech, to complete existing systems and to provide some hints about underlying articulatory gestures recruited during growth to reach perceptual vowel targets in French.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".