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Improving Robustness of Speaker Verification by Fusion of Prompted Text-Dependent and Text-Independent Operation Modalities

  • Iosif Mporas
  • Saeid Safavi
  • Reza Sotudeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9811)

Abstract

In this paper we present a fusion methodology for combining prompted text-dependent and text-independent speaker verification operation modalities. The fusion is performed in score level extracted from GMM-UBM single mode speaker verification engines using several machine learning algorithms for classification. In order to improve the performance we apply clustering of the score-based data before the classification stage. The experimental results indicated that the fusion of the two operation modes improves the speaker verification performance both in terms of sensitivity and specificity by approximately 2 % and 1.5 % respectively.

Keywords

Speaker verification Fusion Machine learning 

Notes

Acknowledgement

This work was partially supported by the H2020 OCTAVE Project entitled “Objective Control for TAlker VErification” funded by the EC with Grand Agreement number 647850. The authors would like to thank Dr Md Sahidullah, Dr Nicholas Evans and Dr Tomi Kinnunen for their support in this work.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Engineering and TechnologyUniversity of HertfordshireHatfieldUK

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