Relationship Between Jaw Stiffness and Kinematic Variability in Speech
Bibliographic record
Abstract
Humans produce speech by controlling a complex biomechanical apparatus to achieve desired speech sounds. We show here that kinematic variability in speech may be influenced by patterns of jaw stiffness. A robotic device was used to deliver mechanical perturbations to the jaw to quantify its stiffness in the mid-sagittal plane. Measured jaw stiffness was anisotropic. Stiffness was greatest along a protrusion-retraction axis and least in the direction of jaw raising and lowering. Consistent with the idea that speech movements reflect directional asymmetries in jaw stiffness, kinematic variability during speech production was found to be high in directions in which stiffness is low and vice versa. In addition, for higher jaw elevations, stiffness was greater and kinematic variability was less. The observed patterns of kinematic variability were not specific to speech-similar patterns appeared in speech and nonspeech movements. The empirical patterns of stiffness were replicated by using a physiologically based model of the jaw. The simulation studies support the idea that the pattern of jaw stiffness is affected by musculo-skeletal geometry and muscle-force-generating abilities with jaw geometry being the primary determinant of the orientation of the stiffness ellipse.
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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 | 0.010 |
| 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".