Audio Features Selection for Automatic Height Estimation from Speech

  • Todor Ganchev
  • Iosif Mporas
  • Nikos Fakotakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6040)

Abstract

Aiming at the automatic estimation of the height of a person from speech, we investigate the applicability of various subsets of speech features, which were formed on the basis of ranking the relevance and the individual quality of numerous audio features. Specifically, based on the relevance ranking of the large set of openSMILE audio descriptors, we performed selection of subsets with different sizes and evaluated them on the height estimation task. In brief, during the speech parameterization process, every input utterance is converted to a single feature vector, which consists of 6552 parameters. Next, a subset of this feature vector is fed to a support vector machine (SVM)-based regression model, which aims at the straight estimation of the height of an unknown speaker. The experimental evaluation performed on the TIMIT database demonstrated that: (i) the feature vector composed of the top-50 ranked parameters provides a good trade-off between computational demands and accuracy, and that (ii) the best accuracy, in terms of mean absolute error and root mean square error, is observed for the top-200 subset.

Keywords

height estimation from speech speech parameterization feature ranking feature selection SVM regression models 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Todor Ganchev
    • 1
  • Iosif Mporas
    • 1
  • Nikos Fakotakis
    • 1
  1. 1.Artificial Intelligence Group, Wire Communications Laboratory, Dept. of Electrical and Computer EngineeringUniversity of PatrasRion-PatrasGreece

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