Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence

Abstract

With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038<0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.

Keywords

• artificial intelligence
• multi-scale attention mechanism
• speech features
• speech recognition
• human-computer symbiosis english teaching model
• 97a40