Visual scanning of a talking face when evaluating segmental and prosodic information
Bibliographic record
Abstract
Prior work has shown that the mouth area can yield articulatory features of speech segments and durational information (Navarra et al., 2010), while pitch and speech amplitude, are cued by the eyebrows and other head movements (Hamarneh et al., 2019). It has been reported that adults will look more at the mouth when evaluating speech information in a non-native language (Barenholtz et al., 2016). In the present study, we ask how listeners' visual scanning of a talking face is affected by task demands that specifically target prosodic and segmental information, which has not been examined by the prior work. Twenty-five native English speakers heard two audio sentences in English (the native language) or Mandarin (the non-native language) that might differ in segmental or prosodic information, or even both, and then saw a silent video of a talking face. Their task was to judge whether the video matched either the first or second audio sentence (or whether both sentences were the same).The results show that although looking was generally weighted towards the mouth, reflecting task demands, increased looking to the mouth predicted correct responses only for Mandarin trials. This effect was more pronounced in the Prosody and Both conditions, relative to the Segment condition (p < 0.05). The results suggest a link between mouth-looking and the extraction of speech-relevant information at both prosodic and segmental levels, but only under high cognitive load.
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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 | 0.005 |
| 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) | 0.004 | 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".