Application of Phonetics and Phonology in Secondary Level Education for Reducing Bengali Impact on English Pronunciation in Bangladesh
Bibliographic record
Abstract
The manuscript aimed at delineating the importance of introducing phonetics and phonology in the ESL syllabus of primary and secondary level studies in Bangladesh so that learners of all levels could reduce Bengali's impact on English pronunciation. In terms of English as Lingua Franca (ELF), every mother tongue plays interference with English as a Second Language (ESL) pronunciation in each geographical community. In this respect, pronouncing English like the natives is a big challenge for non-native speakers around the world. The scenarios seem all alike in Bangladesh, where a major part of its teachers and learners of the English language cannot pronounce English with IPA standard like the native English speakers. Their excessive mother tongue-centred attitudes appear as hindrances on the way to standard accents. Thus, the learners of Bangladesh have been lagging behind the international communications. The research work was conducted in mixed method type where quantitative was predominating to make the article reliable. Tow data collection tools: questionnaire survey and content analysis were used in this study. The author hoped to conclude by showing that the study of phonetics and phonology at the elementary and secondary levels of ESL may be a more effective strategy to lessen Bengali interference in ESL pronunciation and that Bangladeshi students might be able to speak IPA standard ESL pronunciation. Similarly, if any country used the terminology mentioned above at the same levels of education, they might be able to lessen the influence of their native tongue on their pronunciation of ESL and use the IPA standard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".