Investigating Pronunciation Errors Produced by Gujarati Native Speakers
Bibliographic record
Abstract
Abstract Pronunciation is generally an area of second language learning that is not given sufficient emphasis. Throughout the state of Gujarat, India, in addition to the lack of adequate attention to English language pronunciation in ESL classrooms, another factor that compounds the difficulties with pronunciation involves ESL teachers, who themselves are improperly trained in this area. This study investigates English pronunciation errors produced by a specific group of Gujarati Native Speakers (GNS) in Toronto, Canada, and suggests both written and oral methods to improve pronunciation. This group of GNS consists of twenty-five females and twenty-five males between the ages of twenty and twenty-nine who have lived and worked in Toronto, Canada for a period of two to nine years. They received a formal education in Gujarat and began learning ESL before the age of six years. These GNS, after being immersed in an English-speaking environment, have specific issues with respect to pronunciation skills. The purpose of this research is to assist educators to first improve their own English pronunciation, thereby enabling them to correctly teach English pronunciation to GNS. The study tested three hypotheses: 1) GNS have difficulty pronouncing the English phones [ej], [ɔ], [æ], [i], [ow], [u], [ɾ], [th], [ph], [oj], [aw], [aj], [ð] and [ɵ]; 2) both written and oral methods improve the pronunciation of these phones; and 3) written methods are more effective than oral methods. The two methods employed in this research incorporate the use of a written and an oral method. The written method uses the Gujarati script to transliterate English words and the oral method consists of oral repetition of English words. With respect to the first hypothesis, the results indicated that the above sounds were consistently mispronounced by GNS. Written and oral methods that were suggested for instructors proved to be successful in teaching the correct English pronunciation as depicted by the International Phonetic Alphabet (IPA), thereby supporting the second hypothesis. The written method proved to be slightly more effective than the oral method, but the difference between the written and oral methods was not statistically significant. As a result, the third hypothesis was not supported. Suggestions for improving pronunciation include supplementary language training for teachers of GNS, and additional exposure to English for GNS outside the classroom environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".