The Effects of L2 Experience and Vowel Context on the Perceptual Assimilation of English Fricatives by L2 Thai Learners
Bibliographic record
Abstract
The aim of this study is to investigate the effect of vowel context and language experience in the perceived similarity between L2 English fricatives and Thai sounds. The target English sounds being investigated were the sounds /v, f, w, θ, tʰ, s, ð, d, z, ʃ, t͡ʃ/. These sounds were elicited from four native English speakers in words in onset position and followed by three vowel contexts: high, low and back. Subjects were 54 Thai students divided into two groups: English-major and non-English-major. These Thai learners were asked to identify the sounds they heard with the Thai sounds that were closest in their perception. The findings showed that 1) all shared sounds were matched with the same L1 categories, suggesting that the existence of L2 sounds in the L1 sound system supports the perception; 2) most non-shared sounds, except English /θ/ were matched to Thai sounds that were suggested in previous literature; 3) the perceived similarity of English /θ/ and the L1 Thai sounds showed the effect of the vowel context in that this sound was mostly matched with Thai /f/ in the high and low vowel contexts whereas in the back vowel context, it was matched with Thai /s/; 4) the perceived similarities of both shared and non-shared sounds were affected by vowel context and language experience. The findings of this study shed light on the importance exploring perceived similarities and differences in the phonetic level rather than the phonological one.
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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 | 0.000 |
| 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".