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DOI: 10.14569/IJACSA.2021.0120422
PDF

Indonesian Speech Emotion Recognition using Cross-Corpus Method with the Combination of MFCC and Teager Energy Features

Author 1: Oscar Utomo Kumala
Author 2: Amalia Zahra

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

  • Abstract and Keywords
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Abstract: Emotion recognition is one of the widely studied topics in speech technology. Emotions that come from speech can contain useful information for many purposes. The main aspects in speech emotion recognition are speech features, speech corpus, and machine learning algorithms as the classifier method. In this paper, cross-corpus method is used to conduct Indonesian Speech Emotion Recognition (SER) along with the combination of Mel Frequency Cepstral Coefficients (MFCC) and Teager Energy features. Using Support Vector Machine (SVM) as classifier, the experiment result shows that applying cross-corpus method by adding corpora from other languages to the training dataset improves the emotion classification accuracy by 4.16% on MFCC Statistics feature and 2.09% on Teager-MFCC Statistics feature.

Keywords: Cross corpus; Indonesian speech emotion recognition; Mel Frequency Cepstral Coefficients; Teager Energy

Oscar Utomo Kumala and Amalia Zahra, “Indonesian Speech Emotion Recognition using Cross-Corpus Method with the Combination of MFCC and Teager Energy Features” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120422

@article{Kumala2021,
title = {Indonesian Speech Emotion Recognition using Cross-Corpus Method with the Combination of MFCC and Teager Energy Features},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120422},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120422},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Oscar Utomo Kumala and Amalia Zahra}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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