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DC poleHodnotaJazyk
dc.contributor.authorMatoušek, Jindřich
dc.contributor.authorTihelka, Daniel
dc.contributor.authorPsutka, Josef
dc.date.accessioned2015-12-16T13:28:42Z-
dc.date.available2015-12-16T13:28:42Z-
dc.date.issued2003
dc.identifier.citationMATOUŠEK, JindŘich; TIHELKA, Daniel; PSUTKA, Josef. Automatic segmentation for czech concatenative speech synthesis using statistical approach with boundary-specific correction. In: Eurospeech 2003 - Interspeech: proceedings of the 8th European Conference on Speech Communication and Technology, 1-4 September 2003, Geneva, Switzerland. [Baixas]: ISCA, 2003, p. 301-304. ISSN 1018-4074.en
dc.identifier.issn1018-4074
dc.identifier.urihttp://hdl.handle.net/11025/17031
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/MatousekJ_2003_Automatic
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherISCAen
dc.rights© Jindřichich Matoušek - Daniel Tihelka - Josef Psutkacs
dc.subjectsegmentace řeči s využitím HMMcs
dc.subjectposouvání­ HTK hraniccs
dc.subjectinicializace HMMcs
dc.subjecthranice specifické pro statistické korekcecs
dc.subjectobjektivní­ srovnánícs
dc.titleAutomatic segmentation for czech concatenative speech synthesis using statistical approach with boundary-specific correctionen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper deals with the problems of automatic segmentation for the purposes of Czech concatenative speech synthesis. Statistical approach to speech segmentation using HMMs is applied in the baseline system. Several improvements of this system are then proposed to get more accurate segmentation results. These enhancements mainly concern the various strategies of HMM initialization (flat-start initialization, hand-labeled or speaker independent HMM bootstrapping). Since HTK was utilized in our work, a correction of the output boundary placements is proposed to reflect speech parameterization mechanism. An objective comparison of various automatic methods and manual segmentation is performed to find out the best method. The best results were obtained for boundary-specific statistical correction of the segmentation that resulted from bootstrapping with hand-labeled HMMs (96% segmentation accuracy in tolerance region 20ms).en
dc.subject.translatedHMM-based speech segmentationen
dc.subject.translatedshifting HTK boundariesen
dc.subject.translatedHMM initializationen
dc.subject.translatedboundary-specific statistical correctionen
dc.subject.translatedobjective comparisonen
dc.type.statusPeer-revieweden
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