Prespeech tongue posture reflects upcoming speech motor demands: Evidence from ultrasound and electromagnetic articulography
Bibliographic record
Abstract
Speakers maintain distinct postures of the vocal tract in between utterances, however, it remains unclear to what degree these postures are influenced by upcoming motor demands of speech movements. We report two experiments assessing whether prespeech tongue postures changed depending on the motor demands of upcoming speech sounds. First, we employed EMA on the Haskins Production Rate Comparison database, and found that tongue positions between 200 and 100ms reflected the height and backness of the upcoming vowel onset. Next, we used ultrasound imaging to assess the timecourse of postural change in between utterances, and found that interspeech rest postures were influenced by the upcoming vowel much earlier, with task-specificity of the posture increasing further as the onset approached. Qualtitatively, we observe that task-specific properties are overlaid on top of neutral postural substrates such as the clinical resting position. These results demonstrate that pre-speech postures account for upcoming motor demands, supporting previous observations of task-specificity in prespeech posture. These results also identify further commonalities between postural control in gross and fine motor skills.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".