Computer simulation of the vocal tract in speech production
Bibliographic record
Abstract
Neuro-musculoskeletal function can be well characterized for most human movements, such as walking, through a combination of experimental measurements, including motion capture, electromyography, and force sensing. Movements of the vocal tract, however, pose a considerable challenge in terms of holistically measuring and visualizing the neuro-musculoskeletal processes underlying speech production. This is due to the inaccessibility of the vocal tract, the large number of small, deep muscles involved, the fast speed of movements, and the inherent three-dimensional nature of vocal tract shape during speech production. An array of measurement and imaging modalities have been tailored to vocal tract measurement, such as MRI, EMA, EPG, and ultrasound, each providing valuable, but incomplete information regarding vocal tract movements. Computer simulation of the vocal tract can play an important role in complementing these sparse experimental measurements by aiding in the fusion of multiple imaging modalities, helping to describe and visualize the 3D structures of interest, filling in the gaps in experimental measurements (both spatial and temporal), and extrapolating from the small sample sizes commonly found in speech production studies. We will discuss two types of computer simulations that are emerging as important complementary forms of speech production investigation: forward biomechanical simulation of the vocal tract articulators and probabilistic simulation of neuro-musculoskeletal parameters.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".