Cross-Linguistic Study on VOT of Chinese Trilingual Speakers
Bibliographic record
Abstract
In the field of third language acquisition (TLA) in China, only a few empirical researches were carried out to discuss the negative transfer from the mother tongue, so this work aimed to examine that if the cross-linguistic influence (CLI) from L2 exists. We measured and analyzed 8 Chinese college students’ voice onset time (VOT) of stops /p t k b d g/ in English (L2) and Italian (L3). Two Italian native speakers’ VOT values were taken as the reference group. The result shows that Chinese students can hardly distinguish unaspirated voiceless stops /p t k/ and voiced stops /b d g/ in Italian because students are affected by Chinese (L1)’s stop system which is characterized by aspiration. Pre-voicing was observed in voiced stops /b d g/ in both L2 and L3. The analysis of variance shows they are similar (P>0.05). Based on this result, we discussed the possibility to develop the Speech Learning Model (SLM) which was brought out on account of second language acquisition of phonetics and adopt it to explain the learning of L3 speech.
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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 | 0.001 | 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".