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DC poleHodnotaJazyk
dc.contributor.authorPsutka, Josef V.
dc.contributor.authorVaněk, Jan
dc.contributor.authorPsutka, Josef
dc.date.accessioned2016-01-06T13:29:22Z
dc.date.available2016-01-06T13:29:22Z
dc.date.issued2011
dc.identifier.citationPSUTKA, Josef V.; VANĚK, Jan; PSUTKA, Josef. Speaker-clustered acoustic models evaluated on GPU for on-line subtitling of parliament meetings. In: Text, speech and dialogue. Berlin: Springer, 2011, p. 284-290. (Lectures notes in computer science; 6836). ISBN 978-3-642-23537-5.en
dc.identifier.isbn978-3-642-23537-5
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/JosefVPsutka_2011_Speaker-clustered
dc.identifier.urihttp://hdl.handle.net/11025/17133
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture notes in computer science; 6836en
dc.rights© Josef V. Psutka - Jan Vaněk - Josef Psutkacs
dc.subjectakustické modelovánícs
dc.subjectGPUcs
dc.titleSpeaker-clustered acoustic models evaluated on GPU for on-line subtitling of parliament meetingsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper describes the effort with building speaker-clustered acoustic models as a part of the real-time LVCSR system that is used more than one year by the Czech TV for automatic subtitling of parliament meetings broadcasted on the channel ČT24. Speaker-clustered acoustic models are more acoustically homogeneous and therefore give better recognition performance than single gender-independent model or even gender-dependent models. Frequent changes of speakers and a direct connection of the LVCSR system to the audio channel require an automatic switching/fusion of models as quickly as possible. An important part of the solution is real time likelihood evaluations of all clustered acoustic models, taking advantage of a fast GPU(Graphic Processing Unit). The proposed method achieved a WER reduction to the baseline gender-independent model over 2.34% relatively with more than 2M Gaussian mixtures evaluated in real-time.en
dc.subject.translatedacoustic modelsen
dc.subject.translatedGPUen
dc.identifier.doi10.1007/978-3-642-23538-2_36
dc.type.statusPeer-revieweden
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