Speech adaptation to palatal perturbation: Evidence for sensorimotor reorganization across the workspace
Bibliographic record
Abstract
Competent speakers demonstrate a high degree of precision in speech production, but also considerable flexibility in speech motor patterns used to attain speech goals. This flexibility is evident in studies examining adaptation to sensory perturbations, including physical manipulations that alter both auditory and somatosensory feedback (e.g., using a palatal prosthesis to disrupt the production of the alveolar fricative /s/). While the acoustic effects of such perturbations (and subsequent adaptation) have been well explored, their underlying articulatory basis remains poorly understood. In this study, we explored speech adaptation to a palatal perturbation in 9 adult speakers using acoustic and articulatory measures of adaptation (using electromagnetic articulography) during the production of various consonants and vowels. Acoustic measures showed effects of both the perturbation and subsequent compensation (following 15 minutes of speech practice) across the consonants and vowels. Analysis of tongue kinematics confirmed the global nature of these effects, including significant changes in sounds involving no physical interaction with the palate. Furthermore, directional analyses revealed a degree of fine-tuning in motor adaptation across phonemes. The findings indicate that even localized changes in palate shape induce complex compensatory changes in speech motor control across the articulatory workspace.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".