Bibliographic record
Abstract
Studies show that auditory-training improves non-native listeners’ tonal identification; however, persistently perceptual confusion of several tone pairs is still observed. This may imply that learners have not yet mastered/acquired the lexical tones. Their confusion may be reduced further if the essential acoustic information of tones (e.g., duration and pitch contour) could be implemented and emphasized during training. To verify the assumption, the present study examines the impact of employing acoustic information of lexical tones as feedback on non-native listeners’ performance during a computer-based perception training of Mandarin tones. Non-native speakers of Mandarin were randomly assigned to one of two groups. Listeners in the control group were merely shown that the answer was right or wrong. In contrast, those in the experimental group received acoustic information by means of both visual and auditory feedback when the response was incorrect (i.e., showing pitch graphs and presenting the audio files for the contrastive tonal pairs). Results indicated a significant improvement in the tonal identifications for listeners who received detailed acoustic information during training. This suggests that training with acoustic information of lexical tones assists non-native listeners in distinguishing the tone pairs more effectively. [Work supported by SSHRC.]
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".