Coarticulatory effects of lateral tongue bracing in first and second language English speakers
Bibliographic record
Abstract
This study uses electromagnetic articulometry (EMA) to examine the coarticulatory effects of tongue bracing in L1 and L2 English speakers. The tongue is hydrostatic, so we brace it against our teeth for added control, and this bracing is an important part of pronunciation. The amount of bracing may differ across languages (and may be part of one’s articulatory setting), so understanding these differences could be a key to L2 pronunciation learning. Although lingual coarticulation has been examined using acoustics and midsagittal views of the vocal tract, not much focus has been placed on the coronal view. We collected EMA point-tracking data from two native speakers of North American English and looked at the movement of a lateral tongue marker. As stimuli, we choose the nursery rhyme “Mary had a Little Lamb” because of the variation in vowels, and also the /l/ and /r/ phonemes, which are absent in Japanese. Initial results show differences between vowels that occur next to /l/ and those that occur next to /r/ and stops. Results will also be presented for Japanese speakers of both their L1 (Japanese) and L2 English. If we find crosslinguistic differences in bracing, this fact will be important for pedagogical purposes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".