Perturbed palatal shape and North American English /r/ production
Bibliographic record
Abstract
It is well established that the lowered F3 associated with the acoustic percept of American English /r/ can be achieved with different tongue shapes in production. Broadly speaking these shapes may be grouped into ‘‘bunched’’ and ‘‘retroflex’’ varieties. In this work the effects of somatosensory perturbation on /r/ production are examined. Subjects were fitted with a custom palatal prosthesis incorporating a 0.5 cm protrusion along the alveolar ridge, and tongue position during production of /r/ in vocalic contexts was observed using EMA under four conditions: before prosthesis placement; while wearing the prosthesis immediately following placement; still wearing the prosthesis following an unrecorded 20 min adaptation period; and immediately after prosthesis removal. Acoustic effects of the perturbation were minimal, especially after adaptation; production effects were most pronounced in the low vowel context. One subject showed an unperturbed preference for a retroflex configuration, but increased the degree of retroflexion with the palatal prosthesis in place. The remaining subjects preferred unperturbed bunched shapes, but under the perturbed conditions produced primarily retroflex configurations, which for one subject persisted after prosthesis removal. These results suggest that speaker preference for one shape over another may be determined by palatal morphology. [Work supported by NIH.]
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".