Articulatory setting as global coarticulation: Simulation, acoustics, and perception
Bibliographic record
Abstract
Articulatory settings, language-specific default postures of the speech articulators, have been difficult to distinguish from segmental speech content [see Gick et al. 2004, Phonetica 61, 220-233]. The simplest construal of articulatory setting is as a constantly maintained set of tonic muscle activations that coarticulates globally with all segmental content. In his early Overlapping Innervation Wave theory, Joos [1948, Language Monogr. 23] postulated that all coarticulation can be understood as simple overlap, or superposition [Bizzi et al. 1991, Science 253, 287-291], of muscle activation patterns. The present paper describes an implementation of Joos’ proposals within a modular neuromuscular framework [see Gick & Stavness 2013, Front. Psych. 4, 977]. Results of a simulation and perception study will be reported in which muscle activations corresponding to English-like and French-like articulatory settings are simulated and superposed on activations for language-neutral vowels using the ArtiSynth biomechanical modeling toolset (www.artisynth.org). Simulated visible and acoustic outputs presented to perceivers familiar with both languages speak to the question of whether overlapping muscle activations generate outputs that look and sound language-appropriate to perceivers, testing a unified, context-independent model for both coarticulation and articulatory setting. [Research funded by NIH Grant DC-02717 and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".