Speech Production of Mandarin Lexical Tones Among Canadian Elementary Students Enrolled in Mandarin–English Bilingual Schools
Bibliographic record
Abstract
PURPOSE: This study investigates how Mandarin-English bilingual students in Canada produce Mandarin tones and how this is influenced by factors such as tone complexity, cross-linguistic influences, and speech input. METHOD: Participants were 82 students enrolled in a Chinese bilingual program in Western Canada. Students were recruited from Grades 1, 3, and 5 and divided into two groups based on their home language backgrounds: The heritage language group had early and strong input in Mandarin, and the second language (L2) group received mostly English input at home. Single-word tone productions were audio-recorded and transcribed by Mandarin-native listeners for match (accuracy) and pattern analyses. Acoustic measurements were extracted to provide phonetic details. RESULTS: First, Tone3 (dipping tone) was challenging across groups due to its complexity. Second, L2 students' productions were more influenced by English as a nontonal language and showed signs of categorical confusion. Third, increased tone match rates were related to both home input and school input, but bilingual students did not reach more than 90% of match rates in Grade 5. Instead, L2 students produced phonetic features less accurately in higher grades. This was attributed to reduced pronunciation instruction and limited home input. CONCLUSIONS: Bilingual students' speech development in a minority language indicates unique influences of home and school input but also the universal influences of tone complexity. This study provides evidence for bilingual speech theories in the suprasegmental domain and has implications for the pedagogy of a minority language in the context of bilingual education. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28098206.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".