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dc.contributor.authorGuner, Ekrem
dc.contributor.authorDemiroğlu, Cenk
dc.date.accessioned2016-07-26T12:21:39Z
dc.date.available2016-07-26T12:21:39Z
dc.date.issued2012
dc.identifier.issn2165-0608
dc.identifier.urihttp://hdl.handle.net/10679/4279
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6204745&tag=1
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractThe HMM-based TTS (HTS) approach has been increasingly getting more attention from the TTS research community. One of the advantage is the lack of spurious errors that are observed in the unit selection scheme. Another advantage of the HTS system is the small memory footprint requirement which makes it attractive for embedded devices. Here, we propose a novel hybrid statistical unit selection TTS system for agglutinative languages that aims at improving the quality of the baseline HTS system while keeping the memory footprint small. The intelligibility and quality scores of the baseline system are comparable to the MOS scores of English reported in the Blizzard Challenge tests. Listeners preferred the hybrid system over the baseline system in the A/B preference tests.
dc.description.sponsorshipTÜBİTAK
dc.language.isoturen_US
dc.publisherIEEE
dc.relationinfo:turkey/grantAgreement/TUBITAK/109E281
dc.relation.ispartof2012 20th Signal Processing and Communications Applications Conference (SIU)
dc.rightsrestrictedAccess
dc.titleEklemeli̇ di̇ller i̇çi̇n düşük bellekli̇ melez i̇stati̇sti̇ksel/bi̇ri̇m seçmeli̇ MKS si̇stemi̇en_US
dc.title.alternativeA small footprint hybrid statistical/unit selection TTS synthesis system for agglutinative languages
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID & YÖK ID 144947) Demiroğlu, Cenk
dc.contributor.ozuauthorDemiroğlu, Cenk
dc.identifier.startpage1
dc.identifier.endpage4
dc.identifier.doi10.1109/SIU.2012.6204745
dc.subject.keywordsHidden Markov models
dc.subject.keywordsNatural language processing
dc.subject.keywordsSpeech intelligibility
dc.subject.keywordsSpeech synthesis
dc.identifier.scopusSCOPUS:2-s2.0-84863465087
dc.contributor.ozugradstudentGuner, Ekrem
dc.contributor.authorMale2


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