The influence of visual information on the perception of Japanese-accented speech
Bibliographic record
Abstract
This study examines how visual information in nonnative speech affects native listener judgments of second language (L2) speech production. Native Canadian English listeners 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. Among the stimuli, /v, θ, l, ɹ/ are not existent in the Japanese consonant inventory. These stimuli were presented under audio-visual (AV), audio-only (AO), and visual-only (VO) conditions. The results showed that while overall perceptual judgments of the nonnative phonemes (/v, θ, l, ɹ/) were significantly less intelligible than the native phonemes (/b,s/), the English listeners perceived the Japanese productions of the phonemes /v, θ, b,s/ as significantly more intelligible when presented in the AV condition compared to the AO condition. However, the Japanese production of /ɹ/ was perceived as less intelligible in the AV compared to the AO condition. Further analysis revealed that a significant number of Japanese productions of /ɹ/ lacked lip-rounding, indicating that nonnative speakers’ incorrect articulatory configurations may decrease intelligibility. These results suggest that visual cues in L2 speech productions may be either facilitative or inhibitory in native perception of L2 accented-speech. [Research supported by SFU and 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 | 0.000 |
| 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".