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Relevance Vector Machine Based Speech Emotion Recognition

  • Fengna Wang
  • Werner Verhelst
  • Hichem Sahli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6975)

Abstract

This work aims at investigating the use of relevance vector machine (RVM) for speech emotion recognition. The RVM technique is a Bayesian extension of the support vector machine (SVM) that is based on a Bayesian formulation of a linear model with an appropriate prior for each weight. Together with the introduction of RVM, aspects related to the use of SVM are also presented. From the comparison between the two classifiers, we find that RVM achieves comparable results to SVM, while using a sparser representation, such that it can be advantageously used for speech emotion recognition.

Keywords

Support Vector Machine Feature Selection Emotion Recognition Feature Selection Technique Relevance Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fengna Wang
    • 1
  • Werner Verhelst
    • 1
    • 2
  • Hichem Sahli
    • 1
    • 3
  1. 1.AVSP, Department ETROVrije Universiteit BrusselBrusselsBelgium
  2. 2.Interdisciplinary Institute for Broadband Technology - IBBTGhentBelgium
  3. 3.Interuniversity Microelectronics Centre - IMECBrusselsBelgium

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