Presentation Enhancing Chinese EFL Learners’ Connected Speech Through AI-integrated Training more

Recent years have seen increasing integration of AI tools in pronunciation training with proven effectiveness. However, their application to connected speech processes (CSPs) remains underexplored. English CSPs, which involve various types of sound adjustments, are widely used by native speakers in daily communication but pose substantial challenges for Chinese EFL learners in both perception and production. This study aims to develop and evaluate an AI-enhanced CSP training package comprising three components: explicit instruction, perception practice, and production practice. The package integrated materials generated by Murf (a text-to-speech tool) and feedback from Doubao (a generative AI chatbot). Its effectiveness was evaluated by comparing 18 intermediate-level Chinese university students with Northern Mandarin as L1 on sentence dictation and sentence reading-aloud tasks before and after eight online training sessions. Results indicated that participants’ perception and production of CSPs improved significantly (p < .001). However, the training effects exhibited an asymmetrical pattern across the six target CSP types (i.e., consonant-vowel linking, elision, vowel-vowel linking, assimilation, vowel reduction, and multiple) and between perception and production tasks. These findings support incorporating CSP instruction into regular English classrooms and highlight the potential benefits of strategic AI tool implementation in CSP-focused pronunciation training.