Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

Using Speech Emotion Recognition to Preclude Campus Bullying

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_59,
        author={Jianting Guo and Haiyan Yu},
        title={Using Speech Emotion Recognition to Preclude Campus Bullying},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={MFCC PCA KNN Speech emotion recognition Campus bullying},
        doi={10.1007/978-3-030-32388-2_59}
    }
    
  • Jianting Guo
    Haiyan Yu
    Year: 2019
    Using Speech Emotion Recognition to Preclude Campus Bullying
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_59
Jianting Guo1,*, Haiyan Yu1
  • 1: Harbin Institute of Technology at Weihai
*Contact email: guojianting0616@163.com

Abstract

Campus bullying could have extremely adverse impact on pupils, leading to physical harm, mental disease, or even ultra behaviour like suicide. Hence, an accurate and efficient anti-bullying approach is badly needed. A campus bullying detection system based on speech emotion recognition is proposed in this paper to distinguish bullying situations from non-bullying situations. Initially, a Finland emotional speech database is divided into two parts, namely training-data and testing-data, from which MFCC (Mel Frequency Cepstrum Coefficient) parameters are garnered. Subsequently, ReliefF feature selection algorithm is applied to select the useful features to form a matrix. Then its dimensions is diminished with PCA (Principle Component Analysis) algorithm. Finally, KNN (K-Nearest Neighbor) algorithm is utilized to train the model. The final simulations show a recognition rate of 80.25%, verifying that this model is able to provide a useful tool for bullying detection.