Examining Speech Perception–Production Relationships Through Tone Perception and Production Learning Among Indonesian Learners of Mandarin
Bibliographic record
Abstract
BACKGROUND: A transfer of learning effects across speech perception and production is evident in second-language (L2)-learning research, suggesting that perception and production are closely linked in L2 speech learning. However, underlying factors, such as the phonetic cue weightings given to acoustic features, of the relationship between perception and production improvements are less explored. To address this research gap, the current study explored the effects of Mandarin tone learning on the production and perception of critical (pitch direction) and non-critical (pitch height) perceptual cues. METHODS: This study tracked the Mandarin learning effects of Indonesian adult learners over a four-to-six-week learning period. RESULTS: We found that perception and production gains in Mandarin L2 learning concurrently occurred with the critical pitch direction cue, F0 slope. The non-critical pitch height cue, F0 mean, only displayed a production gain. CONCLUSIONS: The results indicate the role of critical perceptual cues in relating tone perception and production in general, and in the transfer of learning effects across the two domains for L2 learning. These results demonstrate the transfer of the ability to perceive phonological contrasts using critical phonetic information to the production domain based on the same cue weighting, suggesting interconnected encoding and decoding processes in L2 speech learning.
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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) | 0.000 | 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 | 0.001 |
| 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".