Speaking Tongues Are Actively Braced
Bibliographic record
Abstract
Purpose: Bracing of the tongue against opposing vocal-tract surfaces such as the teeth or palate has long been discussed in the context of biomechanical, somatosensory, and aeroacoustic aspects of tongue movement. However, previous studies have tended to describe bracing only in terms of contact (rather than mechanical support), and only in limited phonetic contexts, supporting a widespread view of bracing as an occasional state, peculiar to specific sounds or sound combinations. Method: The present study tests the pervasiveness and effortfulness of tongue bracing in continuous English speech passages using electropalatography and 3-D biomechanical simulations. Results: The tongue remains in continuous contact with the upper molars during speech, with only rare exceptions. Use of the term bracing (rather than merely contact) is supported here by biomechanical simulations showing that lateral bracing is an active posture requiring dedicated muscle activation; further, loss of lateral contact for onset /l/ allophones is found to be consistently accompanied by contact of the tongue blade against the anterior palate. In the rare instances where direct evidence for contact is lacking (only in a minority of low vowel and postvocalic /l/ tokens), additional biomechanical simulations show that lateral contact is maintained against pharyngeal structures dorsal to the teeth. Conclusion: Taken together, these results indicate that tongue bracing is both pervasive and active in running speech and essential in understanding tongue movement control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".