Conference paper

BOUSQUET Pierre-Michel, LARCHER Anthony, MATROUF Driss, BONASTRE Jean-Francois and PLCHOT Oldřich. Variance-Spectra based Normalization for I-vector Standard and Probabilistic Linear Discriminant Analysis. In: Proceedings of Odyssey 2012: The Speaker and Language Recognition Workshop. Singapur: International Speech Communication Association, 2012, pp. 157-164. ISBN 978-981-07-3093-2. Available from: http://www.fit.vutbr.cz/research/groups/speech/publi/2012/bousquet_odyssey2012_157-164-09.pdf
Publication language:english
Original title:Variance-Spectra based Normalization for I-vector Standard and Probabilistic Linear Discriminant Analysis
Title (cs):Normalizace I-vektorů na základě variance spektra pro standardní a pravděpodobnostní lineární diskriminační analýzu
Pages:157-164
Proceedings:Proceedings of Odyssey 2012: The Speaker and Language Recognition Workshop
Conference:Odyssey 2012: The Speaker and Language Recognition Workshop
Place:Singapur, SG
Year:2012
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2012/bousquet_odyssey2012_157-164-09.pdf
ISBN:978-981-07-3093-2
Publisher:International Speech Communication Association
Keywords
i-vectors, probabilistic linear discriminant analysis, speaker recognition
Annotation
This paper is on various i-vector normalizations for speaker recognition using standard and probabilistic Linear Discriminant Analysis (LDA and PLDA)
Abstract
I-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) has become the state-of-the-art configuration for speaker verification. Recently, Gaussian-PLDA has been improved by a preliminary length normalization of i-vectors. This normalization, known to increase the Gaussianity of the i-vector distribution, also improves performance of systems based on standard Linear Discriminant Analysis (LDA) and "two-covariance model" scoring. We propose in this paper to replace length normalization by two new techniques based on total, between- and within-speaker variance spectra 1. These "spectral" techniques both normalize the i-vectors length for Gaussianity, but the first adapts the i-vectors representation to a speaker recognition system based on LDA and two-covariance scoring when the second adapts it to a Gaussian-PLDA model. Significant performance improvements are demonstrated on the male and female telephone portion of NIST SRE 2010. Index Terms: i-vectors, probabilistic linear discriminant analysis, speaker recognition.
BibTeX:
@INPROCEEDINGS{
   author = {Pierre-Michel Bousquet and Anthony Larcher and Driss Matrouf
	and Jean-Francois Bonastre and Old{\v{r}}ich Plchot},
   title = {Variance-Spectra based Normalization for I-vector Standard
	and Probabilistic Linear Discriminant Analysis},
   pages = {157--164},
   booktitle = {Proceedings of Odyssey 2012: The Speaker and Language
	Recognition Workshop},
   year = {2012},
   location = {Singapur, SG},
   publisher = {International Speech Communication Association},
   ISBN = {978-981-07-3093-2},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10054}
}

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