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Neural Network for Bullying Emotion Recognition

  • Xinran Zhou
  • , Liang Ye*
  • , Chenguang He
  • , Tapio Seppänen
  • , Esko Alasaarela
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • University of Oulu
  • Ministry of Public Security of the People's Republic of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Bullying is a kind of aggressive behaviour of unjustified actions and speeches which usually occur among students. Bullying behaviours are often followed by verbal disputes such as abuse, crying and other negative voices. Bullying emotion recognition can assist the detection of bullying behaviours. This paper uses neural networks for speech emotion recognition to recognize bullying behaviours. Spectrograms and MFCC features are extracted from speeches, and CNN and RBF neural networks are used for classification. According to experimental results, CNN outperforms RBF and gets an average recognition accuracy of 82.5% for bullying emotions.

Original languageEnglish
Title of host publicationCommunications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
EditorsQilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Xiaoxia Li, Baoju Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages379-383
Number of pages5
ISBN (Print)9789811584107
DOIs
StatePublished - 2021
Event9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 - Changbaishan, China
Duration: 4 Jul 20205 Jul 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume654 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Country/TerritoryChina
CityChangbaishan
Period4/07/205/07/20

Keywords

  • Convolutional neural networks
  • Radial basis function
  • Speech emotion recognition

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