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Fuzzy Logic Applied for Pronunciation Assessment

Fuzzy Logic Applied for Pronunciation Assessment
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Author(s): Halima Bahi (University Badji Mokhtar, Annaba, Algeria) and Khaled Necibi (University of Constantine 2 (MISC Laboratory), La Nouvelle Ville Ali Mendjeli, Algeria)
Copyright: 2020
Volume: 10
Issue: 1
Pages: 13
Source title: International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT)
Editor(s)-in-Chief: Bin Zou (Xi'an Jiaotong-Liverpool University, China) and Michael Thomas (University of Central Lancashire, UK)
DOI: 10.4018/IJCALLT.2020010105

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Abstract

Pronunciation teaching is an important stage in language learning activities. This article tackles the pronunciation scoring problem where research has demonstrated relatively low human-human and low human-machine agreement rates, which makes teachers skeptical about their relevance. To overcome these limitations, a fuzzy combination of two machines scores is suggested. The experiments were carried in the context of Algerian pupils learning to read Arabic. Although the native language of Algerian pupils is a dialect of Arabic, Modern Standard Arabic remains difficult for them with difficult sounds to master and letters close in their pronunciation. The article presents a fuzzy evaluation system including both oral reading fluency, and intelligibility. The fuzzy system has shown that despite the disparities between human ratings, its scores correspond at least to one of their ratings and most of the time its ratings are in favor of learners. Therefore, fuzzy logic, more favorable than thresholding systems, encourages learners to pursue their training.

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