Adaptation to structural modifications of the human vocal tract during speech: Electropalatographic measures
Bibliographic record
Abstract
Structural modifications to the vocal tract force speakers to alter their previously learned articulatory patterns in order to produce perceptually adequate speech. Previous research has shown that acoustic output in the production of alveolar consonants changes during adaptation to structural alterations of the palate, but to date, little is known regarding exactly how these changes result kinematically. The present study examines the adjustments made to tongue–palate contact patterns, measured using electropalatography (EPG), during adaptation to a palatal perturbation for the fricative [s]. Productions of the nonsense word [asa] were elicited in nine subjects at five time intervals, 15 min apart, while speakers wore electropalates modified with a thicker-than-normal alveolar ridge. Between measurement intervals, speakers read [s]-laden passages to promote adaptation. Productions were also elicited with an unperturbed electropalate in place to characterize normal articulation. Electropalatographic analyses revealed a posterior shift in center of gravity of tongue–palate contact, alterations in the width of the medial groove necessary for [s] production, and increased variability in productions, which may reflect the instability of the new motor programs. Results are discussed in relation to the development of adaptive articulatory programs in speech motor control. [Work supported by NSERC and a FRSQ Bourse de Formation.]
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".