Speech-like movements emerge from simulated peri-oral muscle activation space without neural control
Bibliographic record
Abstract
Muscle synergies have been proposed as a solution to the degrees of freedom problem inherent in the biomechanics of speech production [Gick & Stavness, 2013, Front. Psych, 4, 977]. However, the majority of experiments involving the extraction of muscle synergies prove theoretically insufficient to determine whether such synergies are of neural origin or simply reflect the lower dimensionality of an under-sampled biomechanical/neural task space [Kutch & Valero-Cuevas, 2012, PLoS computational biology, 8(5), e1002434]. Therefore, to what extent biomechanics of the human vocal tract may constrain the “ecological state space” of a speaker during locution remains uninvestigated. As a proof of concept, we created a simplified version of the peri-oral region using FEM modeling in a physics-based simulator (ArtiSynth [Lloyd et al., 2012, Soft tissue biomech…surgery, pp. 355]). Systematic simulations enabled us to model the full kinematic/biomechanical space. Visualization of the resulting biomechanical state space using t-SNE [van der Maaten & Hinton, 2008, J.MLR., 9, 2579] illustrates that speech-like movements (e.g., lip rounding or spreading) emerge as self-organizing structures (muscle synergies) critically without a direct neural controller. These results are discussed in the context of current and future explorations of motor control of speech. [Funded by NSERC.]
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".