Role of linguistic experience on audio-visual perception of English fricatives in quiet and noise backgrounds
Bibliographic record
Abstract
Previous research shows that for native perceivers, visual information enhances speech perception, especially when auditory distinctiveness decreases. This study examines how linguistic experience affects audio-visual (AV) perception of non-native (L2) speech. Native Canadian English perceivers and Mandarin perceivers with two levels of English exposure (early and late arrival in Canada) were presented with English fricative-initial syllables in a quiet and a caf-noise background in four ways: audio-only (A), visual-only (V), congruent AV, and incongruent AV. Identification results show that for all groups, performance was better in the congruent AV than A or V condition, and better in quiet than in caf-noise background. However, whereas Mandarin early arrivals approximate the native English patterns, the late arrivals showed poorer identification, more reliance on visual information, and greater audio-visual integration with the incongruent AV materials. These findings indicate that although non-natives were more attentive to visual information, they failed to use the linguistically significant L2 visual cues, suggesting language-specific AV processing. Nonetheless, these cues were adopted by the early arrivals who had more L2 exposure. Moreover, similarities across groups indicate possible perceptual universals involved. Together they point to an integrated network in speech processing across modalities and linguistic backgrounds. [Work supported by SSHRC.]
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".