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AuthorKara Kayikci, Zöhredc.contributor.author
Date of accession2016-03-15T11:04:19Zdc.date.accessioned
Available in OPARU since2016-03-15T11:04:19Zdc.date.available
Year of creation2008dc.date.created
AbstractIn this thesis a novel hybrid approach to automatic speech recognition (ASR) has been proposed. This hybrid system is based on hidden Markov models (HMMs) on the subword-unit level and neural associative memories (NAMs) on the word and language levels. The focus of the work is to develop a flexible and robust speech recognition system against real-world environments and to augment the recognition performance. The developed hybrid system consists of two parts: HMM-based subword-unit recognition and NAM based word recognition, which is also composed of single word recognition network and language model network. The developed hybrid system is also a part of a language processing system embedded in a mobil robot. For a given speech utterance the developed hybrid system recognizes words and forwards them to the NAM based sentence understanding module in the language processing system. Within the scope of this thesis different features of the developed hybrid system were investigated. These features include representation and handling of ambiguities on different levels and incremental extension of task vocabulary with novel words. The proposed hybrid speech recognition system has been successfully applied to various recognition tasks. Compared to other HMM-based speech recognition systems in the literature, competitive recognition results were achieved.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (Fassung vom 01.10.2008)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
KeywordNeural associative memoriesdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHHidden Markov modelsdc.subject.lcsh
LCSHWord recognitiondc.subject.lcsh
TitleWord recognition using hidden Markov models and neural associative memoriesdc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3889dc.identifier.doi
PPN621070408dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-71993dc.identifier.urn
GNDWorterkennungdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2010-02-22T14:21:26Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 13.541; W: W-H 11.976uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS-ID7199uulm.vtsID
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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