Empirical Approaches to Measuring the Intelligibility of Different Varieties of English in Predicting Listener Comprehension
Bibliographic record
Abstract
Abstract This study compared five research‐based intelligibility measures as they were applied to six varieties of English. The objective was to determine which approach to measuring intelligibility would be most reliable for predicting listener comprehension, as measured through a listening comprehension test similar to the Test of English as a Foreign Language. The speakers included 18 English users representing six distinct varieties. These speakers’ speech was evaluated by 60 listeners, users of the same English varieties who completed the listening comprehension test as well as five intelligibility tasks, all recorded by the speakers. The five measures of intelligibility included responses to true/false statements, scalar ratings of speech, perception of nonsense sentences, perception of filtered sentences, and transcription of speech; these measures were compared in terms of their relationship to listening comprehension scores using linear mixed‐effects models. Results showed that the measure of intelligibility based on listeners’ responses to nonsense sentences was the strongest predictor of the listening comprehension scores. Open Practices This article has been awarded an Open Materials badge. Study materials are publicly accessible in the IRIS digital repository at http://www.iris-database.org . Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki .
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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 | 0.026 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".