Evaluating how fine-grained changes in the spatial and temporal properties of audiovisual speech influence the perception of linguistic meter
Bibliographic record
Abstract
The visual information that contributes to speech perception has been known for over a half century (Sumby and Pollack, JASA, 1954). More recent work has demonstrated two important constraints on the benefit of visual information to speech perception. First, the benefit patterns with temporal constraints on general audiovisual sensory processing, and degrades when auditory and visual signals are misaligned by an offset on the order of the duration of a syllable. Second, studies have demonstrated that low spatiotemporal frequency visual information is sufficient for boosting speech intelligibility. Collectively, these findings suggest that visual information facilitates processing of linguistic information organized in the speech stream primarily at the level of the syllable. Our experiments address how manipulation of the phasing and amplitude of visual components of the audiovisual stream associated with reiterant speech affects the perception of linguistic stress. Our analyses focus on determining the sensitivity of speech perception to changes in the fine-grained structure of visible motion (kinematics) and its alignment with the speech acoustics.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".