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DC poleHodnotaJazyk
dc.contributor.authorKolář, Jáchym
dc.contributor.authorLiu, Yang
dc.contributor.authorShriberg, Elizabeth
dc.date.accessioned2016-01-06T14:01:43Z
dc.date.available2016-01-06T14:01:43Z
dc.date.issued2009
dc.identifier.citationKOLÁŘ, Jáchym; LIU, Yang; SHRIBERG, Elizabet. Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations. In: Acoustics, Speech and Signal Processing, 2009. ICASSP ´09, 19-24 April 2009 Taipei. Beijing: IEEE Press, 2009, p. 4701-4704. ISBN 978-1-4244-2353-8 .en
dc.identifier.isbn978-1-4244-2353-8
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/JKolar_2009_Genreeffectson
dc.identifier.urihttp://hdl.handle.net/11025/17135
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEE Pressen
dc.rights© Jáchym Kolář - Yang Liu - Elizabeth Shribergcs
dc.subjectporozumění mluvené řečics
dc.subjectsegmentace větcs
dc.subjectrozhlasové zprávycs
dc.subjectrozhlasové konverzacecs
dc.subjectprozodiecs
dc.titleGenre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversationsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains – broadcast news (BN) and broadcast conversation (BC). We employ an HMM model based on textual and prosodic information and analyze differences in segmentation accuracy and feature usage between the two genres using both manual and automatic speech transcripts. Experiments are evaluated using Czech broadcast corpora annotated for sentencelike units (SUs). Prosodic features capture information about pause, duration, pitch, and energy patterns. Textual knowledge sources include words, part-of-speech, and automatically induced classes. We also analyze effects of using additional textual data that is not annotated for SUs. Feature analysis reveals significant differences in both textual and prosodic feature usage patterns between the two genres. The analysis is important for building automatic understanding systems when limited matched-genre data are available, or for designing eventual genre-independent systems.en
dc.subject.translatedspoken language understandingen
dc.subject.translatedsentence segmentationen
dc.subject.translatedbroadcast newsen
dc.subject.translatedbroadcast conversationsen
dc.subject.translatedprosodyen
dc.type.statusPeer-revieweden
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  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. Fakulta aplikovaných věd / Faculty of Applied Sciences
  4. Katedra kybernetiky / Department of Cybernetics
  5. Články / Articles (KKY)