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Today’s second language (L2) learners have economic and social reasons for learning and speaking English in the global community; these reasons reinforce the necessity for cultivating intelligible English pronunciation competence for successful communication. However, pronunciation instruction has not been intensively incorporated into the language classroom (Acton, Baker, Burri & Teaman, 2013; Couper, 2003). Many English language teachers do not feel at ease when deciding on pronunciation foci, designing interactive pronunciation instruction, and providing individual pronunciation feedback (Levis & Grant, 2003).
The situation is more challenging in English as a Foreign Language (EFL) contexts, where teachers often have limited instructional time and teaching resources while confronting large class sizes with differing levels of students’ English proficiency (Chen & Goh, 2011). It is difficult for EFL teachers to address all the features of pronunciation that need attention within the restricted class time and to provide individual students with appropriate opportunities for practice. Many EFL teachers are not satisfied with the available textbooks, which do not effectively integrate pronunciation with speaking instruction (Hayati, 2010). In addition, having a large number of students limits EFL teachers from providing individualized pronunciation instruction or feedback (Ahn & Lee, 2016; Chang, Yan & Tseng, 2012). As a result, it seems EFL learners almost find themselves lacking time or opportunities to learn and practice their pronunciation (Ahn, & Lee, 2016; Hu, 2003).
The advent and advancement of automatic speech recognition (ASR) technology generates a new arena for English pronunciation teaching and learning (Bahi & Necibi; Elimat & AbuSeileek, 2014). The automatic scoring and feedback hold the potential of facilitating pronunciation assessment, which plays a key role in guiding or adjusting an instructional plan to address students’ needs (Brinton, Celce-Murcia & Goodwin, 2010; Derwing & Munro, 2015). Assisted by the ASR technology, spontaneous pronunciation proficiency assessment and diagnostic feedback are generated, demonstrating the strengths and weaknesses of learners’ English pronunciation. Results from an ASR-based pronunciation assessment could be utilized as useful references to decide on pronunciation teaching foci.
With the help of ASR technology, especially ASR-supported technology integrated with mobile software, mobile-assisted pronunciation training (MAPT) can not only expand the time and space for pronunciation learning but also provide a flexible and personalized learning environment (Kan, 2018; Kang & Ginther, 2017). EFL learners facing limited teaching resources can benefit greatly from MAPT software by using it as an autonomous learning tool to practice pronunciation and receive feedback for further improvement.
A body of studies have focused on investigating the effectiveness of ASR technology on pronunciation improvement (Chun, 2012; Elimat & AbuSeileek, 2014; Liu & Hung, 2016; Sidgi & Shaari, 2017), though the research exploring automatic pronunciation assessment is relatively less substantial. This study investigates the effectiveness of ASR pronunciation software in diagnosing EFL learners’ pronunciation segmental errors. In addition, it also explores the teacher’s and students’ attitudes towards ASR pronunciation software as a pronunciation assessment and learning tool. To this end, this study applies ASR-based pronunciation software to assess EFL learners’ pronunciation errors and to assist learners’ learning of pronunciation. It is hoped that the study may demonstrate how pronunciation assessment and learning in the EFL context can be facilitated by using MAPT software.