Conference paper

MOTLÍČEK Petr, DEY Subhadeep, MADIKERI Srikanth and BURGET Lukáš. Employment of Subspace Gaussian Mixture Models in Speaker Recognition. In: Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. South Brisbane, Queensland: IEEE Signal Processing Society, 2015, pp. 4445-4449. ISBN 978-1-4673-6997-8.
Publication language:english
Original title:Employment of Subspace Gaussian Mixture Models in Speaker Recognition
Title (cs):Využití podprostorových modelů Gaussovských směsí pro rozpoznávání mluvčího
Pages:4445-4449
Proceedings:Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing
Conference:40th International Conference on Acoustics, Speech and Signal Processing is starting
Place:South Brisbane, Queensland, AU
Year:2015
ISBN:978-1-4673-6997-8
Publisher:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2015/motlicek_icassp2015_0004445.pdf [PDF]
Files: 
Keywords
speaker recognition, i-vectors, subspace Gaussian mixture models, automatic speech recognition
Annotation
In this paper, we proposed an alternative approach for speaker recognition based on employment of speaker vectors estimated using the SGMM framework.
Abstract
This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significantly outperform traditional HMM/GMMs in Automatic Speech Recognition (ASR) applications. An extension to the basic SGMM framework allows to robustly estimate low-dimensional speaker vectors and exploit them for speaker adaptation. We propose a speaker verification framework based on low-dimensional speaker vectors estimated using SGMMs, trained in ASR manner using manual transcriptions. To test the robustness of the system, we evaluate the proposed approach with respect to the state-of-the-art i-vector extractor on the NIST SRE 2010 evaluation set and on four different length-utterance conditions: 3sec-10sec, 10 sec-30 sec, 30 sec-60 sec and full (untruncated) utterances. Experimental results reveal that while i-vector system performs better on truncated 3sec to 10sec and 10 sec to 30 sec utterances, noticeable improvements are observed with SGMMs especially on full length-utterance durations. Eventually, the proposed SGMM approach exhibits complementary properties and can thus be efficiently fused with i-vector based speaker verification system.
BibTeX:
@INPROCEEDINGS{
   author = {Petr Motl{\'{i}}{\v{c}}ek and Subhadeep Dey and Srikanth
	Madikeri and Luk{\'{a}}{\v{s}} Burget},
   title = {Employment of Subspace Gaussian Mixture Models in Speaker
	Recognition},
   pages = {4445--4449},
   booktitle = {Proceedings of 2015 IEEE International Conference on
	Acoustics, Speech and Signal Processing},
   year = {2015},
   location = {South Brisbane, Queensland, AU},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-4673-6997-8},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=10952}
}

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