Effects of spatiotemporal manipulation of audiovisual speech on the perception of prosodic structure
Bibliographic record
Abstract
Work in audiovisual speech processing (AVSP) has established that the availability of visual speech signals can influence auditory perception by improving the intelligibility of speech in noise (Sumby and Pollack, 1954). However, exactly which aspects of visible signals are most responsible for this enhancement remains an open question, although convergent evidence along several lines suggests that visible information may reflect a common articulatory-acoustic temporal signature, and that the multi-modal availability of this temporal signature is at the root of this effect. We evaluated this hypothesis in a perceptual study using simple talking face animations whose motion is driven by a signal derived from the collective motion of perioral structures of an actual talker. We applied spatial and temporal manipulations to the structure of this driving signal using a biologically plausible model that preserves the smoothness of the manipulated trajectory, and tested whether these kinematic manipulations influenced the perception of linguistic prominence, an important component of the timing and rhythm (prosody) of speech. The data suggest that perceivers are sensitive to these manipulations, and that the cross-correlation between the acoustic amplitude envelope and the manipulated visible signal was a strong predictor of the perception of prominence.
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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) | 0.002 | 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".