About: Refinement approach for adaptation based on combination of MAP and fMLLR

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	This paper deals with a combination of basic adaptation techniques of Hidden Markov Model used in the speech recognition. The adaptation methods approach the data only through their statistics, which have to be accumulated before the adaptation process. When performing two adaptations subsequently, the data statistics have to be accumulated twice in each of the adaptation passes. However, when the adaptation methods are chosen with care, the data statistics may be accumulated only once, as proposed in this paper. This significantly reduces the time consumption and avoids the need to store all the adaptation data. Combination of Maximum A-Posteriori Probability and feature Maximum Likelihood Linear Regression adaptation is considered. Motivation for such an approach could be the on-line adaptation, where the time consumption is of big importance. This paper deals with a combination of basic adaptation techniques of Hidden Markov Model used in the speech recognition. The adaptation methods approach the data only through their statistics, which have to be accumulated before the adaptation process. When performing two adaptations subsequently, the data statistics have to be accumulated twice in each of the adaptation passes. However, when the adaptation methods are chosen with care, the data statistics may be accumulated only once, as proposed in this paper. This significantly reduces the time consumption and avoids the need to store all the adaptation data. Combination of Maximum A-Posteriori Probability and feature Maximum Likelihood Linear Regression adaptation is considered. Motivation for such an approach could be the on-line adaptation, where the time consumption is of big importance. (en)
Title	Refinement approach for adaptation based on combination of MAP and fMLLR Refinement approach for adaptation based on combination of MAP and fMLLR (en)
skos:prefLabel	Refinement approach for adaptation based on combination of MAP and fMLLR Refinement approach for adaptation based on combination of MAP and fMLLR (en)
skos:notation	RIV/49777513:23520/09:00501702!RIV10-MSM-23520___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(1QS101470516), P(GA102/08/0707), P(LC536)
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Zajíc, Zbyněk Müller, Luděk Machlica, Lukáš
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	338502
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/09:00501702
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	MAP; fMLLR; adaptation; speech recognition; combination (en)
http://linked.open.../riv/klicoveSlovo	fMLLR adaptation MAP speech recognition combination
http://linked.open...ontrolniKodProRIV	[39FCADE966B7]
http://linked.open...v/mistoKonaniAkce	Plzeň
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	Text, Speech and Dialogue
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Speech Recognition under Real-World Conditions Integrated center for natural language processing Automatic keyword spotting in audio data streams
http://linked.open...UplatneniVysledku	2009
http://linked.open...iv/tvurceVysledku	Machlica, Lukáš Zajíc, Zbyněk Müller, Luděk
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open...ain/vavai/riv/wos	000270445700037
http://linked.open.../riv/zahajeniAkce	2009-09-17 (xsd:date)
number of pages	8 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-04207-2
http://localhost/t...ganizacniJednotka	23520

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