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Jaeho Jeon; Seongyong Lee; Serafín M. Coronel-Molina – ELT Journal, 2024
Artificial intelligence (AI) technologies, particularly chatbots with speech-recognition, are gaining attention as tools for ELT. However, this frontline development in contemporary ELT seems to stand in stark contrast to the multilingual effort, another innovative trend, as chatbots' speech recognition capabilities are primarily attuned to native…
Descriptors: Artificial Intelligence, Computer Software, English (Second Language), Second Language Learning
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Lee, Bradford J.; Bailey, Justin L. – Language Awareness, 2023
While listeners tend to downgrade speakers' accent and comprehensibility when they perceive them to be from a different language community--a process known as reverse linguistic stereotyping (RLS)--research has generally relied solely on quantitative data such as Likert scale ratings. The current study sought to extend the analysis further by…
Descriptors: Likert Scales, Stereotypes, Ethnicity, Intelligibility
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Sok, Sarah; Du, Qian; Lee, Jerry Won – International Journal of Bilingual Education and Bilingualism, 2023
Various examinations are administered at universities across the US as a means of assessing bilingual/multilingual international teaching assistants' (ITAs') proficiency in spoken English before they are permitted to teach. While such exams are taken by students in a range of academic disciplines, recent research suggests that communicative…
Descriptors: Oral Language, Language Tests, English (Second Language), Second Language Learning
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Zhiying, Wang – THAITESOL Journal, 2018
This article investigates the effect of different accents on high- and low-proficiency second language (L2) learners. It begins with testing the English listening proficiency of Chinese students and then compares the influence of three accented English (a British accent, an Australian accent and an Indian accent) on their listening scores.…
Descriptors: Pronunciation, Language Proficiency, Preferences, Indians