Electropalatographic and acoustic measures of adaptation to palatal perturbation
Bibliographic record
Abstract
The present investigation examined adaptation to a palatal perturbation in [s] production, as reflected in both electropalatographic (EPG) and acoustic measures. The goal was to explore the development of compensatory motor programs during the production of individual fricative segments, as well as over a 1-h period of focused practice. Thirty repetitions of the syllable [sa] were produced at each of five time intervals (times 0, 15, 30, 45, and 60 min) by a speaker wearing a specially designed pseudopalate with a 6 mm buildup of acrylic at the alveolar ridge. At times 0 and 60, baseline measures of [sa] production with a thin pseudopalate and with no palate in place were also recorded to characterize unperturbed articulation. Acoustic analyses focused on centroid frequencies for the [s] productions, while a wide range of EPG analyses were conducted including measures of groove length and width, location and duration of maximum constriction, and variability in tongue-palate contact patterns throughout the fricative production. Preliminary analyses indicate a high degree of variability throughout the practice period. Results are discussed in relation to the development of adaptive articulatory programs in speech motor control and the articulatory configurations necessary for adequate [s] production. [Work supported by NSERC.]
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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".