The influence of visual speech information on the intelligibility of English consonants produced by non-native speakers
Bibliographic record
Abstract
This study examines how visual speech information affects native judgments of the intelligibility of speech sounds produced by non-native (L2) speakers. Native Canadian English perceivers as judges perceived three English phonemic contrasts (/b-v, θ-s, l-ɹ/) produced by native Japanese speakers as well as native Canadian English speakers as controls. These stimuli were presented under audio-visual (AV, with speaker voice and face), audio-only (AO), and visual-only (VO) conditions. The results showed that, across conditions, the overall intelligibility of Japanese productions of the native (Japanese)-like phonemes (/b, s, l/) was significantly higher than the non-Japanese phonemes (/v, θ, ɹ/). In terms of visual effects, the more visually salient non-Japanese phonemes /v, θ/ were perceived as significantly more intelligible when presented in the AV compared to the AO condition, indicating enhanced intelligibility when visual speech information is available. However, the non-Japanese phoneme /ɹ/ was perceived as less intelligible in the AV compared to the AO condition. Further analysis revealed that, unlike the native English productions, the Japanese speakers produced /ɹ/ without visible lip-rounding, indicating that non-native speakers' incorrect articulatory configurations may decrease the degree of intelligibility. These results suggest that visual speech information may either positively or negatively affect L2 speech intelligibility.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".