Journal article

POVEY Daniel, BURGET Lukáš, AGARWAL Mohit, AKYAZI Pinar, GHOSHAL Arnab, GLEMBEK Ondřej, GOEL Nagendra K., KARAFIÁT Martin, RASTROW Ariya, ROSE Richard, SCHWARZ Petr and THOMAS Samuel et al. The subspace Gaussian mixture model-A structured model for speech recognition. Computer Speech and Language. Amsterdam: Elsevier Science, 2011, vol. 25, no. 2, pp. 404-439. ISSN 0885-2308.
Publication language:english
Original title:The subspace Gaussian mixture model-A structured model for speech recognition
Title (cs):Sub-space gaussovský model - strukturovaný model pro rozpoznávání řeči
Pages:404-439
Book:Computer Speech & Language, Volume 25, Issue 2, April 2011
Place:NL
Year:2011
Journal:Computer Speech and Language, Vol. 25, No. 2, Amsterdam, NL
ISSN:0885-2308
Publisher:Elsevier Science
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2011/povey_csl25_elsevier2011_article_p404_439.pdf [PDF]
URL:http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WCW-50NBNNS-2-F&_cdi=6749&_user=640830&_pii=S088523081000063X&_origin=&_coverDate=04%2F30%2F2011&_sk=999749997&view=c&wchp=dGLbVlz-zSkzS&md5=b7d3a1e0c40cc94760ca24ac5c4ccc71&ie=/sdarticle.pdf [PDF]
Keywords
Speech recognition; Gaussian Mixture Model; Subspace Gaussian Mixture Model
Annotation
Speech recognition based on the Hidden Markov Model-Gaussian Mixture Model (HMM-GMM) framework generally involves training a completely separate GMM in each HMM state.We introduce a model in which the HMM states share a common structure but the means and mixture weights are allowed to vary in a subspace of the full parameter space, controlled by a global mapping from a vector space to the space of GMM parameters.
Abstract
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.
BibTeX:
@ARTICLE{
   author = {Daniel Povey and Luk{\'{a}}{\v{s}} Burget and Mohit Agarwal
	and Pinar Akyazi and Arnab Ghoshal and Ond{\v{r}}ej Glembek
	and K. Nagendra Goel and Martin Karafi{\'{a}}t and Ariya
	Rastrow and Richard Rose and Petr Schwarz and Samuel Thomas},
   title = {The subspace Gaussian mixture model-A structured model for
	speech recognition},
   pages = {404--439},
   booktitle = {Computer Speech \& Language, Volume 25, Issue 2, April
	2011},
   journal = {Computer Speech and Language},
   volume = {25},
   number = {2},
   year = {2011},
   publisher = {Elsevier Science},
   ISSN = {0885-2308},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9670}
}

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