Which Features of Accent affect Understanding? Exploring the Intelligibility Threshold of Diverse Accent Varieties
Bibliographic record
Abstract
Abstract With the ascendency of English as a global lingua franca, a clearer understanding of what constitutes intelligible speech is needed. However, research systematically investigating the threshold of intelligibility has been very limited. In this article, we provide a brief summary of the literature as it pertains to intelligible and comprehensible speech, and then report on an exploratory study seeking to determine what specific features of accented speech make it difficult for global listeners to process. Eighteen speakers representing six English varieties were recruited to provide speech stimuli for two English listening tests. Sixty listeners from the same six English varieties took part in the listening tests, and their scores were then assessed against measurable segmental, prosodic, and fluency features found in the speech samples. Results indicate that it is possible to identify particular features of English speech varieties that are most likely to lead to a breakdown in communication, and that the number of such features present in a particular speakers’ speech can predict intelligibility.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".