Bibliographic record
Abstract
Bracing the tongue against rigid vocal tract surfaces (i.e., teeth or palate) has been suggested to be important in facilitating certain kinds of tongue movements [Stone, J. Acoust. Soc. Am. 81, 2207–2218 (1990)]. However, previous studies have generally sought bracing in only a narrow range of phonetic contexts, resulting in a widespread view of bracing as an occasional state, peculiar to specific sounds or sound combinations. The present study uses electropalatography (EPG) as well as ultrasound imaging and electromagnetic articulometry (EMA) to describe tongue bracing in continuous speech passages, finding that the tongue is almost constantly braced against lateral surfaces during running speech. Analysis of archival data from the male and female speakers of American English in the KayPENTAX Palatometer Database (Model 4333) shows that they brace the tongue continuously, except during a small percentage of low vowels, and during a larger percentage of instances of /l/. Additional measures using all three devices, as well as biomechanical simulations using ArtiSynth (www.artisynth.org), provide further insight, indicating that the tongue also braces against the central palate and/or lower jaw, and that bracing points slide anteroposteriorly across speech sounds. These results suggest that bracing is a constant and necessary aspect of tongue motor control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".