Intrinsic factors of releasing motions in an articulator: On assuming segmental input in speech-production models
Bibliographic record
Abstract
Prominent models of speech production use serial input that is assumed to be commensurate with linguistic segments. The view is that such units underlie a serial activation of aperture motions such as closing and opening motions of the lips in articulating a bilabial stop. This is incompatible with conventional EMG observations showing a single burst of activity of labial adductors at the onset of a close-open cycle. The present study examines the spring-like effects of bilabial compression and pressure on labial opening (release) following a relaxation of the orbicularis oris muscle. Using reiterative series [papapapa] produced at increasing intensities, the range and velocity of opening motions of the lower-lip were correlated with lip compression and oral-pressure. The results for three speakers show that pressure and compression are correlated and that these factors account for 45% to 66% of the variance in velocity of lower-lip opening, and for 47% to 73% of the variance in the range of lower-lip opening. These results complement earlier findings of Abbs and Eilenberg (1976) showing the intrinsic effects of muscle elasticity on opening motions of the lips. Close-open cycles in articulators may not reflect segment-by-segment serial activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".