Conference paper

GHOSHAL Arnab, POVEY Daniel, AGARWAL Mohit, AKYAZI Pinar, BURGET Lukáš, FENG Kai, GLEMBEK Ondřej, GOEL Nagendra K., KARAFIÁT Martin, RASTROW Ariya, ROSE Richard, SCHWARZ Petr and THOMAS Samuel. A novel estimation of feature-space MLLR for full_covariance models. In: Proc. International Conference on Acoustics, Speech, and Signal Processing. Dallas: IEEE Signal Processing Society, 2010, pp. 4310-4313. ISBN 978-1-4244-4296-6. ISSN 1520-6149.
Publication language:english
Original title:A novel estimation of feature-space MLLR for full-covariance models
Title (cs):Inovovaný odhad MLLR v prostoru parametrů pro plně kovarianční modely
Pages:4310-4313
Proceedings:Proc. International Conference on Acoustics, Speech, and Signal Processing
Conference:International Conference on Acoustics, Speech, and Signal Processing 2010
Place:Dallas, US
Year:2010
ISBN:978-1-4244-4296-6
Journal:Proc. International Conference on Acoustics, Speech, and Signal Processing, Vol. 2010, No. 3, Piscataway, US
ISSN:1520-6149
Publisher:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2010/ghoshal_icassp2010_4310.pdf [PDF]
Keywords
Speech recognition, Speaker adaptation, Hidden Markov models, Optimization methods, Linear algebra
Annotation
The paper is on a novel estimation of feature-space MLLR for full-covariance models. We present a new approach for full-covariance Gaussian models.
Abstract
In this paper we present a novel approach for estimating featurespace maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maximizing the likelihood function by repeated line search in the direction of the gradient. We do this in a pre-transformed parameter space such that an approximation to the expected Hessian is proportional to the unit matrix. The proposed algorithm is as efficient or more efficient than standard approaches, and is more flexible because it can naturally be combined with sets of basis transforms and with full covariance and subspace precision and mean (SPAM) models.
BibTeX:
@INPROCEEDINGS{
   author = {Arnab Ghoshal and Daniel Povey and Mohit Agarwal and Pinar
	Akyazi and Luk{\'{a}}{\v{s}} Burget and Kai Feng 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 = {A novel estimation of feature-space MLLR for full-covariance
	models},
   pages = {4310--4313},
   booktitle = {Proc. International Conference on Acoustics, Speech, and
	Signal Processing},
   journal = {Proc. International Conference on Acoustics, Speech, and
	Signal Processing},
   volume = {2010},
   number = {3},
   year = {2010},
   location = {Dallas, US},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-4244-4296-6},
   ISSN = {1520-6149},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9308}
}

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