An Immersive English Teaching System Based on Multi-Modal Signal Processing and Machine Learning

This study proposes an immersive English teaching model that integrates multi-modal signal processing and machine learning algorithms to enhance language learning outcomes. By leveraging technologies such as Virtual Reality (VR), Augmented Reality (AR), and contextualized teaching, the model creates an interactive and personalized learning environment. A multi-modal data collection system is designed to capture students' behavioral data, including speech, gestures, and facial expressions, which are analyzed using machine learning algorithms to provide real-time feedback and adaptive teaching strategies. The effectiveness of the model is evaluated through innovative assessment methods. Results demonstrate significant improvements in students' English proficiency, engagement, and learning sustainability. This research highlights the potential of data-driven, immersive teaching systems to transform traditional English education, offering a more intelligent, efficient, and personalized learning experience. Future work will focus on further integrating advanced technologies to optimize teaching outcomes.