Exploring facial gestures and visual speech articulation cues during the production of Canadian English voiced stops
Bibliographic record
Abstract
Decades of research on visual information for speech has demonstrated the informativeness of visual cues to enhance (Cho et al., 2020; Kawase et al., 2014) and influence (MacDonald and McGurk, 1978) speech perception. However, it is unclear which specific visual cues are spatially and temporally correlated to certain features of speech segments. This study explored visual cues of voicing during the production of Canadian English stops using facial recognition technology (Baltrušaitis et al., 2018) and manual coding (using ELAN 2022). We recorded the audio along with two videos, capturing both front and side views simultaneously, from six native Canadian English speakers. We paid special attention to the throat (larynx), chin, and neck areas which have been understudied by the previous literature. Preliminary data shows expanding movement in the submental triangle and throat during the production of voiced stops compared to voiceless stops. This finding supports tongue body lowering and larynx lowering found in the production of English voiced stops (Westbury, 1983). The comparison between utterance initial stops with post-vocalic stops shows that certain visual cues may be related to phonological voicing categorization irrespective of actual voicing during closure, while others reflect phonetic voicing reality.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".