Biomechanical simulation of lip compression and spreading
Bibliographic record
Abstract
Researchers have proposed that human movements exploit regions of biomechanical stability, allowing targets to be reliably achieved in the face of noisy, everyday conditions (e.g., Loeb 2012). Previous biomechanical simulation studies have demonstrated that this property holds for various speech postures of the lips (Stavness et al. 2013; Gick et al. 2020). These studies, however, have omitted two cross-linguistically common lip postures: compression, where the aperture between the lips is narrowed without accompanying protrusion (e.g., Catford 1982), and spreading, where the corners of the lips are drawn back. Previous empirical work has met with difficulty in quantifying the muscle activations that generate these postures due to the interdigitation of lip muscles (Blair and Smith 1986). The present study presents biomechanical simulation results using the Artisynth platform, which allows movements of the face and vocal tract to be simulated as a function of muscle activation (Lloyd et al. 2012). These simulations identify muscle groupings sufficient to produce lip compression and spreading, and provide insight into which of these groupings generate the quantal properties observed in other lip postures. The results complement past experimental findings, and provide a starting point for future modeling and experimentation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".