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IJMLC 2020 Vol.10(5): 692-699 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.992

Deep Convolutional Neural Networks for Emotion Recognition of Vietnamese

Thuy Dao Thi Le, Loan Trinh Van, and Quang Nguyen Hong

Abstract—Human emotions play a very important role in communication. Emotional speech recognition research brings human–machine communication closer to human-to-human communication. This paper presents the evaluation using ANOVA and T-test for the Vietnamese emotional corpus and using deep convolutional neural networks to recognize four basic emotions of Vietnamese based on this corpus: neutrality, sadness, anger, and happiness. Five sets of characteristic parameters were used as inputs of the deep convolutional neural network in which the mel spectral images were taken and attention was paid to the fundamental frequency, F0, and its variants. Experiments were conducted for these five sets of parameters and for four cases, depending on dependent or independent content and dependent or independent speakers. On average, the maximum recognition accuracy achieved was 97.86% under speaker-dependent and content-dependent conditions. The results of the experiments also show that F0 and its variants contribute significantly to the increased accuracy of Vietnamese emotional recognition.

Index Terms—Corpus, deep convolutional neural network, emotion, T-test, ANOVA, recognition, fundamental frequency, mel spectrum, Vietnamese.

Thuy Dao Thi Le is with Hanoi University of Science and Technology, Vietnam (e-mail: thuydtl@soict.hust.edu.vn).


Cite: Thuy Dao Thi Le, Loan Trinh Van, and Quang Nguyen Hong, "Deep Convolutional Neural Networks for Emotion Recognition of Vietnamese," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 692-699, 2020.

Copyright © 2020 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

  • ISSN: 
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Bimonthly
  • 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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