Deep Learning-Based Co-Evolutionary ASR–TTS Framework for Immersive English Listening and Speaking Training

The growing global emphasis on multilingual communication has brought English listening and speaking training to the forefront of research in computer science and human–computer interaction. Traditional language training approaches, often based on static audio content and scripted dialogues, lack adaptability, provide limited feedback, and fail to reflect real-world contexts, ultimately impeding personalized and effective skill development. To address these challenges, we propose an immersive English training system that integrates advanced speech synthesis (TTS) and automatic speech recognition (ASR) technologies within the audio-interactive linguistic enhancement network (AILEN) architecture, supported by a progressive contextual refinement scheme (PCRS). AILEN employs dual-stream encoder–decoder networks and cross-modal attention bridges to simultaneously enhance auditory comprehension and spoken language production. PCRS introduces a dynamic curriculum learning strategy, adapting task difficulty and feedback granularity in response to individual learner progress. Key innovations include the use of bidirectional cycle-consistency loss, phoneme-level refinement mechanisms, domain adaptation for accent robustness, and self-supervised pretraining, all contributing to improved user engagement and learning outcomes. Experimental evaluations show that the system significantly outperforms traditional baselines in terms of comprehension accuracy, pronunciation fluency, and cross-domain generalization. This paper presents a scalable and adaptive framework for English language training, effectively combining advanced computational techniques with practical language learning needs. It offers strong potential for the development of next-generation intelligent educational systems and more natural, effective human–computer interactions.