Effects of L1 Inventory Size and L2 Experience on L2 Speech Perception: Evidence From Canadian English and Mandarin Learners of Korean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it