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This article is part of the series Biometric Signal Processing.

Open Access Research Article

A Tutorial on Text-Independent Speaker Verification

Frédéric Bimbot1*, Jean-François Bonastre2, Corinne Fredouille2, Guillaume Gravier1, Ivan Magrin-Chagnolleau3, Sylvain Meignier2, Teva Merlin2, Javier Ortega-García4, Dijana Petrovska-Delacrétaz5 and Douglas A Reynolds6

Author Affiliations

1 IRISA, INRIA & CNRS, Rennes Cedex 35042, France

2 LIA, University of Avignon, Avignon Cedex 9 84911, France

3 Laboratoire Dynamique du Langage, CNRS, Lyon Cedex 07 69369, France

4 ATVS, Universidad Politécnica de Madrid, Madrid 28040, Spain

5 DIVA Laboratory, Informatics Department, Fribourg University, Fribourg CH-1700, Switzerland

6 Lincoln Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02420-9108, USA

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EURASIP Journal on Advances in Signal Processing 2004, 2004:101962  doi:10.1155/S1110865704310024

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2004/4/101962


Received:2 December 2002
Revisions received:8 August 2003
Published:21 April 2004

© 2004 Bimbot et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper presents an overview of a state-of-the-art text-independent speaker verification system. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling technique used in most systems, is then explained. A few speaker modeling alternatives, namely, neural networks and support vector machines, are mentioned. Normalization of scores is then explained, as this is a very important step to deal with real-world data. The evaluation of a speaker verification system is then detailed, and the detection error trade-off (DET) curve is explained. Several extensions of speaker verification are then enumerated, including speaker tracking and segmentation by speakers. Then, some applications of speaker verification are proposed, including on-site applications, remote applications, applications relative to structuring audio information, and games. Issues concerning the forensic area are then recalled, as we believe it is very important to inform people about the actual performance and limitations of speaker verification systems. This paper concludes by giving a few research trends in speaker verification for the next couple of years.

Keywords:
speaker verification; text-independent; cepstral analysis; Gaussian mixture modeling

Research Article