Enhancement of visual perception of speech via tactile input
Bibliographic record
Abstract
Motor theories of speech perception predict that perceptual information from modalities other than sound enhance speech perception directly by informing the perceiver of the speaker’s gestures [A. Lieberman and I. Mattingly, Cognition 21, 1–36 (1985)]. Acoustic theories predict that perceptual information from modalities other than sound will only enhance speech perception if there is a learned mapping between the acoustic speech signal speech and that modality [R. Diehl and K. Kluender, Ecol. Psych. 1, 121–144 (1989)]. As normal subjects are unlikely to have learned a mapping between visual and tactile speech information, this study tests whether and how tactile input enhances visual speech perception. In the control condition, perceivers in noise repeat syllables pronounced by a speaker who they can see clearly. Accuracy is judged on the basis of the repeated syllables. In the experimental condition, subjects additionally have their hand on the speaker’s face in the Tadoma position. Results show that subjects are significantly more accurate at perceiving speech when they have both visual and tactile input then when they have visual input alone. In particular, tactile input enhances perceptual accuracy of voice and manner features. [Work supported by NSERC.]
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".