Perturbation and compensation in speech acoustics using a jaw-coupled robot
Bibliographic record
Abstract
Observations were made in three speakers of compensation in formant trajectories in response to jaw perturbations during utterances with the general form /siyCVd/, as in ‘‘see red.’’ Custom dental prostheses were used to help immobilize the head (upper jaw) and couple a computer-controlled robotic device (lower jaw). A 3-Newton perturbation force was applied to the jaw during one out of every five repetitions, selected at random, with half of the perturbations applied downward and half upward. Perturbations were triggered from jaw opening (for CV) exceeding a threshold relative to clench position. Audio (at 10 kHz) and jaw position (at 1 kHz) were recorded concurrently. Individual tokens were extracted using the perturbation threshold for alignment. Formants computed over these intervals show initial deviation from control trajectories and then compensation that begins 60–90 ms after perturbation. Since jaw position does not recover its unperturbed trajectory, compensation presumably is effected through modified tongue movements. The observed behavior is compatible with the function of the DIVA model of speech motor planning, in which corrective motor commands are computed in response to errors between anticipated and produced sensory (auditory and somatosensory) consequences. [Research supported by NIDCD.]
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".