Research on spoken English training and real-time feedback modeling by integrating visual-speech multimodal learning

With the rapid development of artificial intelligence technology, English speaking training and real-time feedback systems based on visual-speech multimodal learning have gradually become a prominent research hotspot in the field of speaking education. This study presents a comprehensive multimodal learning model that integrates both visual and speech information, aiming to significantly improve the efficiency and accuracy of spoken English training. By combining advanced speech recognition technologies with facial expression analysis, the system is able to provide real-time, dynamic feedback on various aspects of students' pronunciation, intonation, speech speed, and even subtle facial expression changes. Furthermore, it offers personalized learning suggestions tailored to the individual needs of each student, enhancing their overall language acquisition experience. The experimental results demonstrate that the proposed model can assess students' speaking performance in a more comprehensive manner compared to traditional unimodal speech training systems. It effectively addresses both linguistic and non-verbal communication elements, fostering greater fluency and confidence in learners. Moreover, the study highlights the transformative potential of multimodal fusion technology in English speaking training, suggesting its vast applications for both educational and professional settings. By improving the interactive experience and providing timely corrective feedback, this system has the ability to revolutionize language learning, making it more engaging, personalized, and effective.