Methods of unsupervised adaptation in online speech recognition

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dc.contributor.authorMachlica, Lukáš
dc.contributor.authorZají­c, Zbyněk
dc.contributor.authorPražák, Aleš
dc.date.accessioned2015-12-17T10:29:07Z
dc.date.available2015-12-17T10:29:07Z
dc.date.issued2009
dc.description.abstract-translatedThis paper deals with adaptation techniques based on maximum likelihood linear transformations, which are well suited for the task of on-line recognition. When transcriptions are available before the system starts running, we are speaking about supervised adaptation. In unsupervised adaptation the transcriptions have to be computed in the first pass of the recognition process. This is often the case in on-line recognition, where data are gathered continuously. Because the system does not work perfectly it is suitable to assign a certainty factor (CF) to each of the transcriptions. Only data that transcriptions have high CF are used for the adaptation. In the on-line recognition, the adaptation (in the sense of updating transformation formulas) has to be performed iteratively whenever the amount of recognized data reaches the pre-specified level. When small amount of adaptation data is available, it is suitable to involve regression trees to cluster similar model parameters. It is quite useful to adapt both speech and silence parameters. Because speech and silence have very different characteristics, we have separated them into two different clusters. Presented methods have been tested on short term recordings and results have proved the suitability of the proposed approach.en
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationMACHLICA, Lukáš; ZAJÍC, Zbyněk; PRAĹ˝ĂK, Aleš. Methods of unsupervised adaptation in online speech recognition. In: SPECOM 2009 Proceedings. St. Petersburg: Institute for Informatics and Automation of RAS (SPIIRAS), 2009, p. 448-453. ISBN 978-5-8088-0442-5.en
dc.identifier.isbn978-5-8088-0442-5
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/LukasMachlica_2009_Methodsof
dc.identifier.urihttp://hdl.handle.net/11025/17044
dc.language.isoenen
dc.publisherInstitute for Informatics and Automation of RAS (SPIIRAS)en
dc.rights© Lukáš Machlica - Zbyněk Zajíc - Aleš Pražákcs
dc.rights.accessopenAccessen
dc.subjectadaptační­ technikycs
dc.subjectpravděpodobnost lineární­ transformacecs
dc.subject.translatedadaptation techniquesen
dc.subject.translatedlikelihood linear transformationsen
dc.titleMethods of unsupervised adaptation in online speech recognitionen
dc.title.alternativeMetody neřízené adaptace online rozpoznávání­ řečics
dc.typečlánekcs
dc.typearticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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