Training Mandarin and Cantonese speakers to identify English vowel contrasts: Long-term retention and effect on production
Bibliographic record
Abstract
Synthesized hVd vowel stimuli and naturally produced CVC minimal pairs by multiple talkers were used to train native Mandarin and Cantonese speakers to identify the English /i/–/I/, /u/–/U/, and /E/–/Q/ contrasts. In the pre- and post-tests, subjects took the identification tests on synthesized and natural stimuli, and were also recorded producing the target vowel contrasts. Results showed that subjects relied on duration cues for the /i/–/I/, contrast more consistently than they did for the other two contrasts. Training effectively shifted their attention from duration to spectral cues. Trainees’ perceptual performance on natural tokens improved significantly from pretest to post-test on all three contrasts. Accuracy in generalization to new words produced by new talkers was comparable to new words by familiar talkers. The effect of perceptual learning was retained 3 months later after the training was completed. Improvement in production was observed but performance differences between pre- and post-test did not reach significance. The findings suggest that increased perceptual accuracy in identifying L2 vowel contrasts through perceptual training may not be sufficient for significant improvement in production accuracy. Future studies may look at combination of simultaneous production and perceptual training for better results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".