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Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

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  • Hariharan Muthusamy
  • Kemal Polat
  • Sazali Yaacob

Abstract

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and -nearest neighbor ( NN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

Suggested Citation

  • Hariharan Muthusamy & Kemal Polat & Sazali Yaacob, 2015. "Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, March.
    • Handle: RePEc:hin:jnlmpe:394083
    DOI: 10.1155/2015/394083
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