Effects of exposure and training on perception of Japanese length contrasts by English listeners
Bibliographic record
Abstract
Native English listeners are known to have difficulty distinguishing Japanese words that contrast in phonemic length, often realized as a contrast in vowel or consonant duration. The present study reports results from a series of experiments investigating the extent to which English listeners’ perception of such length contrasts can be modified with exposure to Japanese and with perceptual identification training. Listeners were trained in a minimal-pair identification paradigm with feedback. A pretest and posttest were also administered, using natural tokens of Japanese words containing various vowel and consonant length contrasts, produced in isolation and in a carrier sentence, at three speaking rates, and by multiple talkers. Results indicated that exposure and perceptual training substantially improved identification accuracy. Even though listeners were trained to identify words contrasting in vowel length only, performance also improved for other contrast types. Furthermore, speaking rate strongly affected performance, but training improved performance at all three rates, even though listeners were trained using only stimuli spoken at a normal rate. These results suggest that non-native listeners are highly susceptible to factors that affect the temporal characteristics of speech, but their perceptual strategies can be modified with exposure and training. [Work supported by NICT and JSPS.]
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".