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Speaker Recognition Using Sparse Representation via Superimposed Features

  • Yashesh Gaur
  • Maulik C. Madhavi
  • Hemant A. Patil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

In this paper, we demonstrate the effectiveness of superimposed features for the purpose of template matching-based speaker recognition using sparse representations. The principle behind our hypothesis is, if the test template approximately lies in the linear span of the training templates of the genuine class, then so does any linear combination of test templates. In this paper, we introduce the notion of superimposed features for the first time. Using our initial trials on the TIMIT database, we have shown that superimposed features can result in reducing the complexity cost by 80 % with a very minor decrease in identification rate by 0.67 % and a minor increase in EER by 0.85 %.

Keywords

Superimposed features sparse representations orthogonal matching pursuit template matching speaker recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yashesh Gaur
    • 1
  • Maulik C. Madhavi
    • 1
  • Hemant A. Patil
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)GandhinagarIndia

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