Native Speakers' Perception of Non - Native English Speech
Bibliographic record
Abstract
This study is aimed at investigating the rating and intelligibility of different non-native varieties of English, namely French English, Japanese English and Jordanian English by native English speakers and their attitudes towards these foreign accents. To achieve the goals of this study, the researchers used a web-based questionnaire which targeted native speakers of English. The materials for this study were a questionnaire for respondents to fill out and tape recordings of six different short stories, each of which was recorded by a non native speaker of English. The first short story was tape- recorded by a male French speaker and the second by a female French speaker. Similarly, the other four short stories were tape-recorded by male and female Japanese and Jordanian speakers respectively. The respondents or raters consisted of 110 native speakers of English (78 females and 32 females); the majority of them from the USA, but there were others from Britain, Canada, and Australia. They were requested to surf the webpage www. englishforeignaccent .com, especially designed by the researchers, fill out the questionnaire and rate the non-native varieties under investigation, and four months later the number of respondents reached one hundred and ten which constituted the sample of the study. Data obtained indicated that the Jordanian accent was considered as the most intelligible, followed by the French then the Japanese English accent. The native speakers also showed significantly more positive attitudes towards Jordanian English than French and Japanese English. Finally, the positive attitude towards Jordanian English was affirmed by the respondents who assigned the Jordanian English speakers to the most prestigious professions such as medicine and teaching.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".