The effect of L1 prosodic backgrounds of Cantonese and Japanese speakers on the perception of Mandarin tones after training
Bibliographic record
Abstract
The present study investigated to what extent ones’ L1 prosodic backgrounds affect their learning of a new tonal system. The question as to whether native speakers of a tone language perform differently from those of a pitch accent language will be addressed. Twenty native speakers of Hong Kong Cantonese (a tone language) and Japanese (a pitch accent language) were assigned to two groups. All of them had had no prior knowledge of Mandarin, and had never received any form of musical training before they participated in the study. Their performance of the identification of Mandarin tones before and after a short-term training was compared. Analysis of listeners’ tonal confusions in the pretest, posttest, and generalization tests revealed that both Cantonese and Japanese listeners had more confusion for two contrastive tone pairs: Tone 1–Tone 4, and Tone 2–Tone 3. Moreover, Cantonese speakers consistently had greater difficulty than Japanese speakers in distinguishing the tones in each pair. These imply that listeners L1 prosodic backgrounds are at work during the process of learning a new tonal system. The findings will be further discussed in terms of the Perceptual Assimilation Model (Best, 1995). [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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".