The Effect of L2 Experience on the Perceptual Assimilation of British English Monophthongs to Thai Monophthongs by L2 Thai Learners
Bibliographic record
Abstract
Perceptual assimilation is a well-known task; however, there is no study on the assimilation pattern of the English monophthongs by L2 Thai learners. The aims of this study are to explore the perceptual assimilation patterns of the British English monophthongs to Thai monophthongs by L2 Thai learners and to examine the effect of L2 experience on this perception. The target British English sounds were /iː, ɪ, e, æ, ɒ, ɑː, ɔː, ʊ, uː, ʌ, ɜː/ in /bVt/ context. The Thai listeners performed an assimilation task by matching these British English monophthongs with their L1 Thai monophthongs. The results showed no difference in the assimilation patterns between the high-experienced and low-experienced groups in the perception of the English /ɪ, e, ɑː, ɔː, ʊ, ʌ, ɜː/. The degree of the perceived similarity in the matching of these vowels to the Thai sound categories between these two groups was not significantly different from one another either. However, English /e/ was mostly perceived as Thai /e/ in the high-experienced group to a greater degree than the low-experienced group. The findings also showed the difference in the assimilation patterns between these two groups, i.e. for English /æ, iː, uː, ɒ/ suggesting the importance of the L2 experience in the exploration of the L2 speech learning research. The implication for L2 sound learning of this study is that having higher number of phonemes in the L1 phonological system than that in the L2 one is less important than the L2 experience.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".