Research Article
Using Speech Emotion Recognition to Preclude Campus Bullying
@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
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.
