Effects of L1 Inventory Size and L2 Experience on L2 Speech Perception: Evidence From Canadian English and Mandarin Learners of Korean
Bibliographic record
Abstract
ABSTRACT This article examines the effects of native language (L1) phoneme inventory size and second language (L2) learning experience on adult learners’ perception of L2 sounds. Perception experiments compared the Korean vowel and coda identification accuracy of 28 English- and 28 Mandarin-speaking learners differing in their amount of university-level Korean language experience. The results showed that the English-speaking learners, whose L1 has a rich vowel and coda inventory, were better at identifying both Korean vowels and coda consonants compared to the Mandarin-speaking learners, who have a relatively small L1 vowel and coda inventory. These findings suggest that learners with a larger phoneme inventory have an advantage in the perception of L2 segments. In the case of L2 experience, results from segment identification tasks were less conclusive. Learners who had more L2 experience (i.e., more experience with the Korean language at a university level) performed better only in the vowel identification task compared to learners with less L2 experience. Results also showed no significant difference between more experienced versus less experienced learners in the case of coda identification. These outcomes indicate that learners’ L2 identification accuracy is influenced by the amount of their L2 experience but the presence and degree of this effect can differ depending on the type of L2 segment regardless of L1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".