Reaction time of Japanese listeners to retroflex and bunched /r/pronunciation by native English speakers
Bibliographic record
Abstract
In this study, we focus on Japanese learners’ reaction time (RT) to retroflex and bunched pronunciation of /r/ in English words spoken by native English speakers. In junior high school, Japanese students generally learn only retroflex pronunciation of /r/. If there is a strong link between production and perception, we would expect those students to be able to perceive retroflex /r/ faster than bunched /r/. We carried out a forced-choice RT experiment for 30 native Japanese listeners and 4 native English controls. This experiment used 2 speakers’ voices (both Canadian English) and 9 minimal pairs of /r/ and /l/ words. Stimuli were spoken words and picture-pairs (two simultaneously presented in each trial). Listeners had to identify the spoken word by choosing the left or right picture. As a result, we could measure whether it is easier to perceive sounds pronounced the same way you speak or not. From the results, we found that the RTs for retroflex and bunched pronunciation of English words spoken by native speakers were not significantly different, even for native listeners. In addition, overall accuracy rates were very low among the Japanese speaking participants (66.3%, compared to 99.7% for the English speaking participants).
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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 | 0.005 |
| 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) | 0.002 | 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".