Social robot assisted music course based on speech sensing and deep learning algorithms

Abstract

In the field of social robot teaching, research has focused on how to use technological means to provide better learning support and personalized interactive experiences. Social robots can interact with students and provide personalized learning support, thereby improving their learning effectiveness and engagement. The speech sensing model of social robots can perceive students’ emotions and feedback in real-time through technologies such as speech recognition and sentiment analysis, thereby providing intelligent responses and guidance. The deep learning recommendation model for music course resources extracts music features through deep learning techniques, and combines session interest extraction techniques to personalized recommend music resources suitable for students’ interests and abilities. By analyzing students’ interests and learning goals, robots can provide music learning resources that meet their needs based on recommendation algorithms, further stimulating their learning interest and enthusiasm. The experimental results show that the use of social robots in the learning environment significantly improves the learning effectiveness and participation of students. Through personalized interaction and intelligent response guidance, students are more likely to understand and master music knowledge, while experiencing joyful and positive learning emotions. The study validated the effectiveness of social robot assisted music courses based on speech sensing and deep learning algorithms, demonstrating its advantages in improving student learning effectiveness and engagement.