Training English listeners to perceive phonemic length contrasts in Japanese
Bibliographic record
Abstract
The present study investigated the extent to which native English listeners' perception of Japanese length contrasts can be modified with perceptual training, and how their performance is affected by factors that influence segment duration, which is a primary correlate of Japanese length contrasts. Listeners were trained in a minimal-pair identification paradigm with feedback, using isolated words contrasting in vowel length, produced at a normal speaking rate. Experiment 1 tested listeners using stimuli varying in speaking rate, presentation context (in isolation versus embedded in carrier sentences), and type of length contrast. Experiment 2 examined whether performance varied by the position of the contrast within the word, and by whether the test talkers were professionally trained or not. Results did not show that trained listeners improved overall performance to a greater extent than untrained control participants. Training improved perception of trained contrast types, generalized to nonprofessional talkers' productions, and improved performance in difficult within-word positions. However, training did not enable listeners to cope with speaking rate variation, and did not generalize to untrained contrast types. These results suggest that perceptual training improves non-native listeners' perception of Japanese length contrasts only to a limited extent.
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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 | 0.001 |
| 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 | 0.000 |
| 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".