Empirical Approaches to Measuring the Intelligibility of Different Varieties of English in Predicting Listener Comprehension. Issue 1 (24th October 2017)

• Record Type: Journal Article
• Authors: Kang, Okim; Thomson, Ron I.; Moran, Meghan
• Is Part Of: Language learning. Volume 68:Issue 1(2018)
• Page Start: 115
• Page End: 146
• Publication Date: 2017-10-24
• Subjects: intelligibility -- nonnative -- English varieties -- comprehension -- listening -- assessment; Language and languages -- Periodicals; Language and languages -- Study and teaching -- Periodicals; Linguistics; 407
• DOI: 10.1111/lang.12270
• Languages: English
• ISSNs: 0023-8333

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 athttp://www.iris-database.org . Learn more about the Open Practices badges from the Center for Open Science:https://osf.io/tvyxz/wiki .