Compensation for a lip-tube perturbation in 4-year-olds: Articulatory, acoustic, and perceptual data analyzed in comparison with adults
Bibliographic record
Abstract
The nature of the speech goal in children was investigated in a study of compensation strategies for a lip-tube perturbation. Acoustic, articulatory, and perceptual analyses of the vowels /y/ and /u/ produced by ten 4-year-old French speakers and ten adult French speakers were conducted under two conditions: normal and with a large tube inserted between the lips. Ultrasound and acoustic recordings of isolated vowels were made in the normal condition before any perturbation, for each of the trials in the perturbed condition, and in the normal condition after the perturbed trials. Data revealed that adult participants moved their tongues in the perturbed condition more than children did. The perturbation was generally at least partly compensated for during the perturbed trials in adults, but children did not show a typical learning effect. In particular, unsystematic improvements were observed during the sequence of perturbed trials, and after-effects were not clear in the articulatory domain. This suggests that children may establish associative links between multisensory phonemic representations and articulatory maneuvers, but those links may mainly rely on trial-to-trial, error-based feedback correction mechanisms rather than on the internal model of the speech production apparatus, as they are in adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".