Effects of L1 prosodic background and AV training on learning Mandarin tones by speakers of Cantonese, Japanese, and English
Bibliographic record
Abstract
The present study aims to address the theoretical and methodological issues of tonal acquisition in a second language (L2). The effects of native (L1) prosodic background and audiovisual (AV) training on the perception of the four Mandarin tones by groups of naïve listeners were examined. The experiment employed a pretest-posttest paradigm. Three listener groups of 10 participants each were recruited: Hong Kong Cantonese (a tone language), Japanese (a pitch-accent language), and English (a str ess-accent language). They were randomly assigned to receive one of two training approaches: Simple or AV Feedback. With the Simple Feedback training approach the responses were evaluated as being correct or incorrect. The AV Feedback consisted of sound files, animated pitch graphs, and a brief message that, in addition to indicating whether the response was correct or not, directed listeners’ attention to the crucial perceptual cues of tones. Following training, a posttest and three generalization tests were administered at two different times. Percent correct scores, perceptual sensitivities to each tone (A-prime), and tonal confusions were analyzed. The results indicated substantial differences in the participants’ perception of Mandarin tones after training. With respect to L1 prosodic background, it was found that listeners’ L1 prosodic systems played a significant role in learning Mandarin tones. The Cantonese tonal system hindered the learning of Mandarin tones, whil e the Japanese pitch-accent system facilitated the establishment of a new tonal system. The English stress-accent system neither helped nor hindered tone learning. The performance of the English listeners was intermediate between that of the Cantonese and Japanese listeners. These findings are consistent with the Perceptual Assimilation Model (PAM), suggesting that perceptual mapping is not restricted to segments, but can be extended to suprasegmentals (lexical tones). With respect to the training approach, learners who received AV Feedback required shorter training periods, and they outperformed learners who received Simple Feedback. These findings imply that the AV training approach employed in the current study facilitates the learning of Mandarin tones and promotes the long-term modification of listeners’ tonal properties of L2 tones, thus providing support for its applicability in the training of other tonal languages.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".