Exploring the intelligibilty of foreign-accented English vowels when ‘‘English’’ is ill-defined
Bibliographic record
Abstract
Many studies of foreign-accented speech have been conducted in second language settings in which learners are assumed to be exposed to a relatively homogeneous non-native sound system. However, foreign language learners, who learn an additional language in a setting where this language is not the primary medium of communication, are frequently exposed to a range of varieties of the target language which may differ considerably with respect to their sound systems. The present study examined and compared the intelligibility of English monophthongs produced by two speaker groups: Native Danes who had learned English as a foreign language (with exposure to different native and non-native varieties) and native English speakers from Australia, the US, and the UK. Ten native Canadian-English listeners, who were familiar with native and non-native accents of English, identified the 11 monophthongs of English produced by the speaker groups in a /bVt/ context. As expected, the listeners’ error patterns were specific for each speaker group. However, reduced intelligibility was observed for much the same vowels irrespective of speaker group. Our results suggest that one source of problems in learning the sounds of English is the heterogeneity of English vowel systems in addition to transfer from the native language.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".