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Artificial Intelligence (AI) rapidly transforms language education, showing promise in literacy development, especially in reading and writing. However, its effectiveness in improving oral skills, which is crucial for communication in our interconnected world, requires further investigation. This study explored integrating corpus and AI technologies to enhance English pronunciation teaching and learning (PTL), encompassing both the production and perception of segmental and suprasegmental features across various English accents. The corpus and AI-aided PTL framework addresses the need for innovative PTL, particularly for diverse learners and growing intercultural communication (IC) demands. Three training sessions were designed. Session 1 introduced a pronunciation corpus with authentic speech data and AI-powered personalized feedback. Session 2 expanded corpus use with text-to-speech (TTS) tool for suprasegmental pronunciation practice. Session 3 focused on perceiving accented English through diverse speech samples from the corpus and TTS tool to enhance global communication competency. Thirty university students participated in an online self-access training course. Pre- and post-training tasks assessed pronunciation performance and perception of accented English. Results revealed significant reduction in inappropriate segmental features, such as substituting/θ/with/f/, and inappropriate suprasegmental features, such as inappropriate pause within chunks (e.g. cried | out). Enhanced understanding of diverse English accents was also observed. Post-training surveys and interviews provided qualitative insights. Participants valued the autonomous learning fostered by AI tools and appreciated exposure to diverse accents, recognizing its importance for global communication. These findings suggest the potential of corpus and AI-driven PTL, warranting further research into their long-term impact and adaptability across diverse learning contexts.
Chen et al. (Fri,) studied this question.