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AuthorWagner, Nicolasdc.contributor.author
Date of accession2018-11-05T15:36:17Zdc.date.accessioned
Available in OPARU since2018-11-05T15:36:17Zdc.date.available
Year of creation2017dc.date.created
Date of first publication2018-11-05dc.date.issued
AbstractIn Spoken Dialogue Systems, two techniques are currently used to create an optimal dialogue policy: hand-crafted rules and statistical procedures basing on machine learning. However, both types are not sufficient in complex areas where only limited training data is available. This thesis thus examines a hybrid approach to dialogue management that intents to combine the benefits of both rule-based and statistical methods. For this purpose, probabilistic rules are employed which depend on unknown parameters. Afterwards, these parameters are trained with supervised learning. Furthermore, the dialogue manager is designed to be adaptive to the user's cultural background and emotional condition as this is supposed to have a crucial influence on the conversational behaviour. The configuration is then investigated in the context of the KRISTINA domain. The conducted experiments reveal that it is possible to include emotional and cultural features in the dialogue management.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordSpoken Dialogue Systemdc.subject
KeywordAdaptive Dialogue Managementdc.subject
Dewey Decimal GroupDDC 000 / Computer science, information & general worksdc.subject.ddc
MeSHAutomatic speech recognitiondc.subject.mesh
MeSHMachine learningdc.subject.mesh
TitleUser-adaptive statistical dialogue management using OpenDialdc.title
Resource typeAbschlussarbeit (Master; Diplom)dc.type
Date of acceptance2018dcterms.dateAccepted
RefereeMinker, Wolfgangdc.contributor.referee
RefereeWeber, Michaeldc.contributor.referee
RefereeMiehle, Julianadc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-10218dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-10275-4dc.identifier.urn
GNDMaschinelles Lernendc.subject.gnd
GNDAutomatische Spracherkennungdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Nachrichtentechnikuulm.affiliationSpecific
InstitutionInstitut für Medieninformatikuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category


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