Identifying Problematic Segmental Features to Acquire Comprehensible Pronunciation in EFL Settings: The Case of Japanese Learners of English
Bibliographic record
Abstract
The present study examines how to identify problematic pronunciation features for particular EFL learners, namely native Japanese speakers (NJs) learning English, to acquire comprehensible pronunciation, and tests the appropriateness of the selection. The study comprises two phases. In the identification phase, eight English-specific segmentals, /æ, f, v, θ, ð, w, l, ɹ/, were selected as the most problematic for NJs by drawing on various cross-linguistic analyses (i.e. a remedial approach) as well as a survey in which the advice of 48 experienced NJ English teachers was examined (i.e. an expert judgment approach). In the experimental phase, the relative influence of these sounds on comprehensibility and accentedness was analyzed. Twenty NJ participants read two types of sentences: sentences containing eight English-specific segmentals and sentences without them. Four native English speakers (NEs) subsequently rated all speech stimuli on a rubric of accentedness and comprehensibility. Significant differences were found between NEs’ ratings of the two types of sentences both in the domain of comprehensibility and accentedness. The results indicate that the eight segmentals determine NEs’ speech perception to a great degree, which in turn provides some support for the validity of the identification procedure (i.e. the combination of the remedial and expert judgment approaches).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".