Individual differences in second language speech perception across tasks and contrasts: The case of English vowel contrasts by Korean learners
Bibliographic record
Abstract
Abstract: The present study examines whether individual differences in second language (L2) learners’ perceptual cue weighting strategies reflect systematic abilities. We tested whether cue weights indicate proficiency in perception using a naturalistic discrimination task as well as whether cue weights are related across contrasts for individual learners. Twenty-four native Korean learners of English completed a two-alternative forced choice identification task on /ɪ/-/i/ and /ɛ/-/æ/ contrasts varying orthogonally in formant frequency and duration to determine their perceptual cue weights. They also completed a two-talker AX discrimination task on natural productions of the same vowels. In the cue-weighting task, we found that individual L2 learners varied greatly in the extent to which they relied on particular phonetic cues. However, individual learners’ perceptual weighting strategies were consistent across contrasts. We also found that more native-like performance on this task – reliance on spectral differences over duration – was related to better recognition of naturally produced vowels in the discrimination task. Therefore, the present study confirms earlier reports that learners vary in the extent to which they rely on particular phonetic cues. Additionally, our results demonstrate that these individual differences reflect systematic cue use across contrasts as well as the ability to discriminate naturally produced stimuli.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".