Visual speech primes open-set recognition of spoken words
Bibliographic record
Abstract
Visual speech perception has become a topic of considerable interest to speech researchers. Previous research has demonstrated that perceivers neurally encode and use speech information from the visual modality, and this information has been found to facilitate spoken word recognition in tasks such as lexical decision (Kim, Davis, & Krins, 2004). In this paper, we used a cross-modality repetition priming paradigm with visual speech lexical primes and auditory lexical targets to explore the nature of this priming effect. First, we report that participants identified spoken words mixed with noise more accurately when the words were preceded by a visual speech prime of the same word compared with a control condition. Second, analyses of the responses indicated that both correct and incorrect responses were constrained by the visual speech information in the prime. These complementary results suggest that the visual speech primes have an effect on lexical access by increasing the likelihood that words with certain phonetic properties are selected. Third, we found that the cross-modality repetition priming effect was maintained even when visual and auditory signals came from different speakers, and thus different instances of the same lexical item. We discuss implications of these results for current theories of speech perception.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".