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MFCC Global Features Selection in Improving Speech Emotion Recognition Rate

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Advances in Machine Learning and Signal Processing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 387))

Abstract

Feature selection is one of the important aspects that contribute most to the emotion recognition system performance apart from the database and the classification technique used. Based on the previous finding, Mel Frequency Cepstral Coefficients (MFCC) are said to be good for emotion recognition purpose. This paper discusses the use of MFCC features to recognize human emotion on Berlin database in the German language. Global features are extracted from MFCC and tested with three classification methods; Naive Bayes, Artificial Neural Network (ANN) and Support Vector Machine (SVM). We investigate the capabilities of MFCC global features using 13, 26 and 39-dimensional cepstral features in recognizing emotions from speech. The result from the experiment will be further discussed in this paper.

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Acknowledgments

Special thanks to Ministry of Education (MOE) and Research Management Centre (RMC), Universiti Teknologi Malaysia providing financial support of this research in FRGS Vot number R.J130000.7828.4F253.

Author information

Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Noor Aina Zaidan & Md Sah Salam

Authors
  1. Noor Aina Zaidan
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  2. Md Sah Salam
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Corresponding author

Correspondence to Noor Aina Zaidan .

Editor information

Editors and Affiliations

  1. University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Ping Jack Soh

  2. Singapore Campus, #05-01 SIT Building, Newcastle University, Singapore, Singapore

    Wai Lok Woo

  3. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    Hamzah Asyrani Sulaiman

  4. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    Mohd Azlishah Othman

  5. University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Mohd Shakir Saat

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© 2016 Springer International Publishing Switzerland

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Zaidan, N.A., Salam, M.S. (2016). MFCC Global Features Selection in Improving Speech Emotion Recognition Rate. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-32213-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32212-4

  • Online ISBN: 978-3-319-32213-1

  • eBook Packages: EngineeringEngineering (R0)

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