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Applying SBL and Non-Linear Dynamics Features for Detecting Deception from Speech Signal

  • Yan Zhou
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

Extracting novel non-linear dynamics (NLD) feature sets and applying SBL classifier for deception detection based speech processing is the primary aim of this study. As the NLD features provide additional information regarding the dynamics and structure of deceptive speech, here, 24 NLD features that show significant correlations to deception are selected. The features have been computed partially, and represent so far unknown acoustical perceptual concepts. After a correlation-filter feature subset selection, Sparse Bayesian Learning (SBL) classification model was trained. SBL algorithm is turned out to gain a satisfactory performance for detecting deceptive speakers on the NLD feature sets. Compared with the classical model of SVM and RBFNN, the proposed model achieves high classification accuracy in detecting deception.

Keywords

deception detection speech signal Sparse Bayesian Learning (SBL) non-linear dynamics (NLD) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yan Zhou
    • 1
    • 2
  1. 1.Department of Communication Technology, Electronic Information Engineering CollegeSuzhou Vocational UniversitySuzhouChina
  2. 2.School of Electronics and Information EngineeringSoochow UniversitySuzhouChina

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