Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification

School bullying is common in school life, which has a negative impact on students’ physical and mental health. Nowadays, the research on school bullying at home and abroad mostly relies on human resources. In this paper, a school bullying detection algorithm based on pattern recognition techniques is presented. The authors firstly collect and pre-process the emotional speech data, and extract the MFCC features. Then they reduce the dimension of features to 6 by the PCA algorithm. They design a two-level SVM model in series with linear kernel for classification. The algorithm proposed in this paper effectively achieves a high recognition performance. The accuracy of the algorithm reaches 86.59% and the F1-Measure reaches 87.36%.