Velum movement in speech and inter-speech pause intervals: a cineradiographic study of French and English speech
Bibliographic record
Abstract
Velum behavior in speech production, particularly with nasal sounds, has been a matter of significant interest to researchers revealing many different factors that affect velum movement during speech events. Few studies, however, have explored velum movement patterns during inter-speech pauses compared to speech segments. To address this gap, we examined the velocity of velum movement during the production of both nasal sounds and inter-utterance pauses. We hypothesized that velum movement patterns differ between these two contexts and that language background may modulate these patterns. We analyzed velum movement in sentence-level speech of Québécois French and English speakers from the Université Laval X-ray videofluorography database. We measured the velopharyngeal opening (VPO) as the distance between the velum's upper surface and the posterior pharyngeal wall. The change in VPO over time served as a proxy for the velocity of velum movement during speech segments and inter-speech pauses. We predicted faster velum movement during speech pauses and also faster movement for English relative to French speakers. Our results show that the velum behaves differently between speech and pause events in terms of the velocity and duration of the movement. In addition, velum behavior differs between languages, indicating language-specific articulatory configurations for the velum.
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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 | 0.001 |
| 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".