A novel estimation of feature-space MLLR for full_covariance models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F10%3APU91964" target="_blank" >RIV/00216305:26230/10:PU91964 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A novel estimation of feature-space MLLR for full_covariance models
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/FR-TI1%2F034" target="_blank" >FR-TI1/034: Multilingual recognition and search in speech for electronic dicionaries</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proc. International Conference on Acoustics, Speech, and Signal Processing
ISBN
978-1-4244-4296-6
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE Signal Processing Society
Place of publication
Dallas
Event location
Dallas
Event date
Mar 14, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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