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.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Guo, J., Yu, H. (2019). Using Speech Emotion Recognition to Preclude Campus Bullying. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_59
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DOI: https://doi.org/10.1007/978-3-030-32388-2_59
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