Categorization of phonemic length contrasts in Japanese by native and non-native listeners
Bibliographic record
Abstract
Previous studies reported that exposure to Japanese and identification training allowed English listeners to improve their identification accuracy of Japanese words with phonemic length contrasts. However, this does not necessarily mean their perceptual properties approach those of native listeners in all respects. Past perceptual studies have shown that native listeners show, for example, an extremely sharp short-to-long boundary and shift of the boundary to adapt to changes in the temporal context, i.e., typically speaking rate variations. To carefully investigate the effects of training on such properties, the present study analyzed the learners’ identification of stimulus continua between word pairs that minimally differed in the length of a phoneme. Overall results showed that English listeners’ boundaries tended to be sharpened by the identification training, but only to a limited extent. On the other hand, the results also showed that English listeners’ boundaries shifted across different speaking rates in ways that resemble native listeners’ adaptation tendencies. The latter result suggests that even non-native listeners can adjust their identification boundaries according to differences in temporal context. Discussion will include both prospects and limitations of listeners’ improvement by the perceptual training in this study. [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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".