Ensemble Models for Enhancement of an Arabic Speech Emotion Recognition System

  • Rached Zantout
  • Samira KlaylatEmail author
  • Lama Hamandi
  • Ziad Osman
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


Ensemble classification model has been widely used in the area of machine learning to enhance the performance of single classifiers. In this paper, we study the effect of employing five ensemble models, namely Bagging, Adaboost, Logitboost, Random Subspace and Random Committee, on a vocal emotion recognition system. The system recognizes happy, angry, and surprise emotion from Arabic natural speech where the highest accuracy among single classifiers is obtained by SMO 95.52%. After applying the ensemble models on 19 single classifiers, the best enhanced accuracy is 95.95% achieved by SMO as well. The highest improvement in accuracy was 19.09%. It was achieved by the Boosting technique having the Naïve Bayes Multinomial as base classifier.


Machine learning Ensemble classifiers Emotion recognition Arabic speech Natural corpus 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rached Zantout
    • 1
  • Samira Klaylat
    • 2
    Email author
  • Lama Hamandi
    • 3
  • Ziad Osman
    • 2
  1. 1.Rafik Hariri UniversityAlmechrefLebanon
  2. 2.Beirut Arab UniversityBeirutLebanon
  3. 3.American University of BeirutBeirutLebanon

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