Smiled speech in a context-invariant model of coarticulation
Bibliographic record
Abstract
Smiling during speech requires concurrent and often conflicting demands on the articulators. Thus, speaking while smiling may be modeled as a type of coarticulation. This study explores whether a context-invariant or a context-sensitive model of coarticulation better accounts for the variation seen in smiled versus neutral speech. While context-sensitive models assume some mechanism for planning of coarticulatory interactions [see Munhall et al., 2000, Lab Phon. V, 9–28], the simplest context-invariant models treat coarticulation as superposition [e.g., Joos, 1948, Language 24, 5–136]. In such a model, the intrinsic biomechanics of the body have been argued to account for many of the complex kinematic interactions associated with coarticulation [Gick et al., 2013, POMA 19, 060207]. Largely following the methods described in Fagel [2010, Dev. Multimod. Interf. 5967, 294–303], we examine articulatory variation in smiled versus neutral speech to test whether the local interactions of smiling and speech can be resolved in a context-invariant superposition model. Production results will be modeled using the ArtiSynth simulation platform (www.artisynth.org). Implications for theories of coarticulation will be discussed. [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) | 0.000 | 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) | 0.002 | 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".