Using gated audiovisual speech perception to identify the temporal onset of coarticulation in production
Bibliographic record
Abstract
The goal of the present work was to develop a sensitive, non-invasive method for accurately assessing the temporal scope of coarticulation in speech production. Building on work in audiovisual speech perception (Munhall and Tohkura, 1998; Moradi et al., 2013), we used a gating paradigm and human judges to identify the temporal onset of anticipatory lip rounding in simple SVO sentences produced by five adult females. The sentences were gated based on acoustic landmarks from the midpoint of the verb through to the midpoint of the object noun. Full sentences were also included. Judges were asked to decide whether the object noun rhymed with rounded “oop” or unrounded “ack.” Results indicated an earlier correct identification of rounding in the audiovisual condition compared to the control, audio-only condition. The audiovisual judgments also provided greater temporal specificity regarding the onset of coarticulation than could obtained with acoustic measurement or with a video-based kinematic measure. Insofar as human judges appear to anticipate and even benefit from head movement in audiovisual speech tasks (Munhall et al., 2004), our method should prove especially useful for measuring the temporal extent of coarticulation in younger, more rambunctious speakers.
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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".