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dc.contributor.advisorIsard, Steve
dc.contributor.authorKing, Simon Alistair
dc.date.accessioned2015-05-06T14:35:25Z
dc.date.available2015-05-06T14:35:25Z
dc.date.issued1998
dc.identifier.urihttp://hdl.handle.net/1842/10380
dc.description.abstractThis thesis introduces a general method for using information at the utterance level and across utterances for automatic speech recognition. The method involves classification of utterances into types. Using constraints at the utterance level via this classification method allows information sources to be exploited which cannot necessarily be used directly for word recognition. The classification power of three sources of information is investigated: the language model in the speech recogniser, dialogue context and intonation. The method is applied to a challenging task: the recognition of spontaneous dialogue speech. The results show success in automatic utterance type classification, and subsequent word error rate reduction over a baseline system, when all three information sources are probabilistically combined.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectautomatic speech recognitionen
dc.titleUsing information above the word level for automatic speech recognitionen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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