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IJMLC 2019 Vol.9(4): 490-495 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.4.831

Emotion Recognition System Based on Hybrid Techniques

Wisal Hashim Abdulsalam, Rafah Shihab Alhamdani, and Mohammed Najm Abdullah

Abstract—Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In addition, a bi-modal system for recognising emotions from facial expressions and speech signals is presented. This is important since one modality may not provide sufficient information or may not be available for any reason beyond operator control. To perform this, decision-level fusion is performed using a novel way for weighting according to the proportions of facial and speech impressions. The results show an average accuracy of 93.22 %.

Index Terms—Emotion recognition, convolutional neural network, tensorflow, ADFES-BIV, WSEFEP, SAVEE.

Wisal Hashim Abdulsalam is with University of Baghdad, Iraq (e-mail: wisal.h@ihcoedu.uobaghdad.edu.iq).


Cite: Wisal Hashim Abdulsalam, Rafah Shihab Alhamdani, and Mohammed Najm Abdullah, "Emotion Recognition System Based on Hybrid Techniques," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 490-495, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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