English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Abstract

With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.