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Research Article | Open Access |

Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients

Author 1: Farah Chenchah Author 2: Zied Lachiri
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 6, No. 11 · Published 2015 · Cited by 26

DOI: https://doi.org/10.14569/IJACSA.2015.061119

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Abstract

Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linear Frequency Cepstral Coefficients (LFCC) and Mel-Frequency Cepstral Coefficients (MFCC). Further, these features are classified using Hidden Markov Model (HMM) and Support Vector Machine (SVM).

Keywords

Mel Frequency Cepstral Coefficients (MFCC) Linear Frequency Cepstral Coefficients (LFCC) Hidden Markov Model (HMM) Support Vector Machine (SVM) emotion recognition

How to Cite this Article

Chenchah, F., & Lachiri, Z. (2015). Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients. International Journal of Advanced Computer Science and Applications, 6(11). https://doi.org/10.14569/IJACSA.2015.061119

Chenchah, Farah, and Zied Lachiri. "Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients." International Journal of Advanced Computer Science and Applications, vol. 6, no. 11, 2015, https://doi.org/10.14569/IJACSA.2015.061119.

@article{Chenchah2015,
  title     = {Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {11},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Farah Chenchah and Zied Lachiri},
  doi       = {10.14569/IJACSA.2015.061119},
  url       = {https://doi.org/10.14569/IJACSA.2015.061119}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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