University of Oulu

Han, T., Zhang, J., Zhang, Z., Sun, G., Ye, L., Ferdinando, H., … Yang, S. (2018). Emotion recognition and school violence detection from children speech. EURASIP Journal on Wireless Communications and Networking, 2018(1).

Emotion recognition and school violence detection from children speech

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Author: Han, Tian1,2; Zhang, Jincheng1; Zhang, Zhu2,3;
Organizations: 1Department of Internet of Things Engineering, Harbin University of Science and Technology, Harbin, China
2Optoelectronics and Measurement Technique Unit, University of Oulu, Oulu, Finland
3Department of Communication Engineering, Harbin University of Science and Technology, Harbin, China
4Department of Automation, Heilongjiang University, Harbin, China
5Department of Electrical Engineering, Petra Christian University, Surabaya, Indonesia
6Physiological signal analysis team, University of Oulu, Oulu, Finland
7Department of Measurement-control technology and instrumentation, Harbin University of Science and Technology, Harbin, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Springer Nature, 2018
Publish Date: 2019-07-02


School violence is a serious problem all over the world, and violence detection is significant to protect juveniles. School violence can be detected from the biological signals of victims, and emotion recognition is an important way to detect violence events. In this research, a violence simulation experiment was designed and performed for school violence detection system. Emotional voice from the experiment was extracted and analyzed. Consecutive elimination process (CEP) algorithm was proposed for emotion recognition in this paper. After parameters optimization, SVM was chosen as the classifier and the algorithm was validated by Berlin database which is an emotional speech database of adults, and the mean accuracy for seven emotions was 79.05%. The emotional speech database of children extracted in violence simulation was also classified by SVM classifier with proposed CEP algorithm, and the mean accuracy was 66.13%. The results showed that high classification performance could be achieved with the CEP algorithm. The classification result was also compared with database of adults, and the results indicated that children and adults’ voice should be treated differently in speech emotion recognition researches. The accuracy of children database is lower than adult database; the accuracy of violence detection will be improved by other signals in the system.

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Series: EURASIP journal on wireless communications and networking
ISSN: 1687-1472
ISSN-E: 1687-1499
ISSN-L: 1687-1472
Volume: 2018
Issue: 1
Article number: 235
DOI: 10.1186/s13638-018-1253-8
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The research presented in this paper was supported by the Heilongjiang Provincial Science and Technology Department of China, Heilongjiang Provincial Education Department of China, and Harbin Municipal Science and Technology Bureau of China.
Copyright information: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.