Speed of velum movement during nasal segments and rest intervals: A cineradiographic study of French and English speech
Bibliographic record
Abstract
While opening/closing of the velopharyngeal port (VPP) in speech has been much studied, the speed of these movements has been largely overlooked. The present study compares opening/closing velocities of the VPP in French and English, testing relation to distance traveled and speech-related versus physiological movements. Running speech samples from nine Quebecois French speakers and four Canadian English speakers were obtained from the Université Laval X-ray videofluorography database [Munhall et al., J. Acoust. Soc. Am., 98(2), 1222–1224 (1995)]. Using ImageJ software, we tracked VPP opening/closing movements during two types of events: phonologically nasal segments and rest intervals between chunks of speech. We calculated velocity of VPP opening/closing during these events and analyzed the data using linear mixed-effects models to identify differences between the nasals and rest intervals as well as for any cross-linguistic differences. Results indicated that: (1) VPP closure was faster following English nasals than French nasals; (2) VPP opening was faster than closure for rest intervals in French; and (3) VPP opening/closing speeds were faster coming into/out of rest position than into/out of nasals in French. Preliminary cross-language observations support a correspondence between velocity and distance. [Work supported by NIH and NSERC.]
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".