Hearing Tones, Missing Boundaries: Cross-Level Selective Transfer of Prosodic Boundaries Among Chinese–English Learners
Bibliographic record
Abstract
Second language (L2) learners often struggle to process prosodic boundaries, which are essential for speech comprehension. This study investigated the nature of these difficulties and how first language (L1) cue-weighting strategies transfer to L2 processing among Chinese (Mandarin)-English learners. The rising pitch that cues English phrase boundaries acoustically overlaps with functionally distinct Chinese lexical tones. Through two experiments comparing Chinese-English learners and native English speakers, we assessed sensitivity across lexical constituent, phrase, and sentence boundaries and manipulated acoustic cues (pause, lengthening, pitch) to estimate their perceptual weights during phrase-boundary identification. L2 learners showed reduced discrimination sensitivity only at the phrase level, performing comparably to native speakers at lexical constituent and sentence boundaries. For phrase boundaries, learners over-relied on pitch and under-relied on pre-boundary lengthening compared to native speakers, though both groups weighted pauses strongly. This selective deficit implicates the transfer of L1 cue-weighting strategies more than a global knowledge deficit. Our findings support a dynamic transfer model where L1 sensitivity to lexical tone transfer of L2 phrase perception, elevating the weight of pitch. While learners show partial adaptation, these results refine the Cue-Weighting Transfer Hypothesis by demonstrating that L2 prosodic acquisition involves both integrated L1 transfer and L2-driven reweighting strategies.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".