Bibliographic record
Abstract
Previous models of coarticulation have used varying combinations of advance planning and on-line calculation of weighted averages to determine how temporally overlapping speech sounds interact [see Farnetani and Recasens, Hbk. Phonet. Sci. Wiley-Blackwell (2010)]. A robust model of coarticulation should be able to predict such local interactions, as well as to describe changes resulting from rate variation, which should influence degree of temporal overlap between adjacent events. Using a model of tongue-jaw-hyoid biomechanics [Stavness et al., J. Biomech. (2012)], the present paper demonstrates that typical cases of lingual coarticulation can be attributed to the intrinsic biomechanics of the human body in an entirely feed-forward model with no additional machinery. Biomechanical modeling outcomes are compared to speech articulations at different speech rates, and show that naturalistic coarticulatory patterns emerge simply by varying degrees of temporal overlap in a biomechanically realistic model. The built-in mechanics of the human body can handle coarticulatory interactions with no extrinsic model at all, save one that identifies a) the right body parts, and b) the time-course of events. Results are interpreted in a “Whole Event” model of speech. [Research 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".