Cross-Linguistic Bracing: A Lingual Ultrasound Study of Six Languages
Bibliographic record
Abstract
Lateral bracing refers to contact of the sides of the tongue along the upper molars or palate; evidence from articulatory analysis of native English speakers as well as 3D biomechanical simulations suggests that bracing involves mechanical support which occurs consistently throughout speech [Gick et al. 2017. J Speech Lang Hear Res. 60(3):494-506]. Release of lateral bracing occurs only during some lateral consonants and low vowels. The current study tests for the presence of active lateral bracing in seven languages: Cantonese, English, Korean, Mandarin, Portuguese, Spanish, and Turkish. Ten native speakers of these languages (2 each for English, Mandarin and Korean and one each for the other languages) read aloud passages of the North Wind and the Sun [Handbook of the IPA, 1999] while a coronal ultrasound video of their tongue was recorded. Tracings were made from still images of the M-mode ultrasound videos, and measurements of the vertical motion of the tongue midline and both edges were taken. The percentage of time the tongue is not laterally braced was calculated. Active lateral bracing is implicated if the left and right edges of the tongue are less variable in vertical motion than midline and/or positioned at a stable baseline height for a larger percentage of time than they are lowered. Preliminary analysis supports the hypothesis that tongue bracing in speech exists regardless of language.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".