The Acquisition of English Lexical Stress by Chinese-speaking Learners: An OT Account
Bibliographic record
Abstract
Lexical stress is an important contributor to foreign accent as well as intelligibility of second language (L2) speech. The present study intends to find out to what extent Chinese-speaking learners whose native language has less evident stress can acquire English lexical stress. A production test was administered to nine advanced Chinese learners of English and nine native English controls, who read aloud 12 types of nonce English nouns. The results showed that the Chinese participants were able to place stress correctly in two-syllable words and three-syllable words with a heavy penultimate syllable. However, irregularity was observed in three-syllable words with a light penultimate syllable, particularly H(eavy)L(ow)L(ow). The results are further interpreted in Optimality Theory. It is argued that the learners’ interlanguage grammar is both negatively and positively influenced by their native language. The constraint only active in Chinese causes the interlanguage to be non-nativelike. By contrast, the shared active constraints facilitate learning. Moreover, the emergence of the constraints in the interlanguage grammar which are inactive in Chinese but active in English provides evidence for the learners’ ability to restructure their interlanguage phonology.
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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 | 0.000 | 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 | 0.000 |
| 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".