Lip protrusion and constriction in anticipation in English and French
Bibliographic record
Abstract
Though lip constriction is acoustically the most robust parameter for rounding, most studies on anticipatory coarticulation relied on the investigation of protrusion via EMG, optoelectrical systems or audiovisual processing. These various methods produced inconsistent anticipatory profiles across studies or even speakers. The Movement Expansion Model (MEM) of Abry and Lallouache has explained vocalic anticipatory coarticulation in four French adults and eight children via lip constriction, showing idiosyncratic behaviours, i.e. personal regression slopes but with a lawful variability validated by high correlation coefficients. In order to describe global anticipatory movement, the current study included measures of both protrusion and constriction over time. Four American English and four Canadian French adults were audio visually recorded, uttering [iC[n]u] sequences, in which C[n] corresponded to a varying number of intervocalic consonants. Lip protrusion was measured via an Optotrak system, and constriction via a Lip-Shape Tracker (from ICP Grenoble). We found that when tested on lip area control, English speakers joined French speakers in a regular MEM temporal pattern, displaying their own anticipatory behaviour, with personal regression slope or expansion coefficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".