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A Tutorial on Text-Independent Speaker Verification (2004)
| Venue: | EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2004:4, 430–451 |
| Citations: | 138 - 13 self |
BibTeX
@MISC{Bimbot04atutorial,
author = {Frédéric Bimbot and Jean-françois Bonastre and Corinne Fredouille and Guillaume Gravier and Ivan Magrin-chagnolleau and Sylvain Meignier and Teva Merlin and Javier Ortega-garcía and Dijana Petrovska-delacrétaz and Douglas A. Reynolds},
title = { A Tutorial on Text-Independent Speaker Verification},
year = {2004}
}
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Abstract
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
Keyphrases
speaker verification speaker verification system text-independent speaker verification gaussian mixture modeling state-of-the-art text-independent speaker verification system cepstral analysis speaker tracking test phase neural network actual performance real-world data speaker modeling technique several extension forensic area on-site application important step commonly speech parameterization support vector machine audio information modular scheme remote application detection error trade-off
