Recently, Web 2.0 and mobile applications have become an endless source of new technological tools that integrate Automatic Speech Recognition (ASR). Their use in learning environments has led to a growing interest by researchers whose studies demonstrate the effectiveness of these tools in relation to acquiring L2 pronunciation, to developing oral proficiency in general, and to providing instantaneous individualized feedback. In this presentation, we will examine different types of corrective feedback (CF) that ASR-based applications can provide and we will report the results of our action research on the use of different ASR-based tools in two pronunciation courses.
Pronunciation has a direct impact on the effectiveness of the communication of L2 learners. However, many of them have difficulties with perception and fail to master the suprasegmental and articulatory features of L2, even once they have reached an advanced level (Baker & Smith 2010; Levy & Strange 2008; Strange 2011). Instructors, along with students, consistently express interest in techniques and strategies for correcting pronunciation and are looking for accessible, and affordable tools in order to make up for the lack of pronunciation resources and to overcome traditional language classroom constraints such as, insufficient time, and limited opportunities for output and individualized corrective feedback (CF) (Neri et al., 2013; Collins & Munoz, 2016). Recently, Web 2.0 and mobile applications have become an endless source of new technological tools that integrate Automatic Speech Recognition (ASR). Their use in learning environments has led to a growing interest by researchers whose studies demonstrate the effectiveness of these new tools in relation to acquiring L2 pronunciation, to developing oral proficiency in general, and to providing instantaneous individualized feedback (Strik et al., 2012; Cucchiarini & Strik, 2013, Liakin et al. 2015, 2017). In this presentation, we will first examine different types of implicit and explicit CF that ASR-based applications can provide and will discuss their impact on the acquisition of L2 pronunciation in light of SLA findings (Lyster, 2004; Ellis et al., 2006; Lee & Lyster, 2016 among others). Second, we will report the results of our action research on the use of three different ASR-based tools, with specific reference to learners’ perceptions of the utility of different types of automatic corrective feedback provided by these applications. To conclude, we will offer avenues of discussion and practical suggestions for the effective and sensible integration of ASR-based applications in the teaching and learning of L2 pronunciation.
