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AutorWagner, Nicolasdc.contributor.author
Aufnahmedatum2018-11-05T15:36:17Zdc.date.accessioned
In OPARU verfügbar seit2018-11-05T15:36:17Zdc.date.available
Jahr der Erstellung2017dc.date.created
Datum der Erstveröffentlichung2018-11-05dc.date.issued
ZusammenfassungIn 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
Spracheendc.language.iso
Verbreitende StelleUniversität Ulmdc.publisher
LizenzStandarddc.rights
Link zum Lizenztexthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
SchlagwortSpoken Dialogue Systemdc.subject
SchlagwortAdaptive Dialogue Managementdc.subject
DDC-SachgruppeDDC 000 / Computer science, information & general worksdc.subject.ddc
MeSHAutomatic speech recognitiondc.subject.mesh
MeSHMachine learningdc.subject.mesh
TitelUser-adaptive statistical dialogue management using OpenDialdc.title
RessourcentypAbschlussarbeit (Master; Diplom)dc.type
Datum der Annahme2018dcterms.dateAccepted
GutachterMinker, Wolfgangdc.contributor.referee
GutachterWeber, Michaeldc.contributor.referee
GutachterMiehle, 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
FakultätFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Nachrichtentechnikuulm.affiliationSpecific
InstitutionInstitut für Medieninformatikuulm.affiliationSpecific
DCMI MedientypTextuulm.typeDCMI
KategoriePublikationenuulm.category


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