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dc.contributor.authorLykartsis, Athanasios-
dc.contributor.authorKotti, Margarita-
dc.contributor.authorPapangelis, Alexandros-
dc.contributor.authorStylianou, Yannis-
dc.date.accessioned2020-02-24T17:44:55Z-
dc.date.available2020-02-24T17:44:55Z-
dc.date.issued2019-02-14-
dc.identifier.isbn978-1-5386-4334-1-
dc.identifier.isbn978-1-5386-4335-8-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10822-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9717-
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.description.abstractIn this paper we investigate the novel use of exclusively audio to predict whether a spoken dialogue will be successful or not, both in a subjective and in an objective manner. To achieve that, multiple spectral and rhythmic features are inputted to support vector machines and deep neural networks. We report results on data from 3267 spoken dialogues, using both the full user response as well as parts of it. Experiments show an average accuracy of 74% can be achieved using just 5 acoustic features, when analysing merely 1 user turn, which allows both a real-time but also a fairly accurate prediction of a dialogue successfulness only after one short interaction unit. From the features tested, those related to speech rate, signal energy and cepstrum are amongst the most informative. Results presented here outperform the state of the art in spoken dialogue success prediction through solely acoustic features.en
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-9530-
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.ddc780 Musikde
dc.subject.otherdialogue successen
dc.subject.otheracoustic featuresen
dc.subject.otherdeep neural networksen
dc.subject.othersupport vector machinesen
dc.titlePrediction of Dialogue Success with Spectral and Rhythm Acoustic Features Using DNNS and SVMSen
dc.typeConference Objecten
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/SLT.2018.8639580en
dcterms.bibliographicCitation.proceedingstitle2018 IEEE Spoken Language Technology Workshop (SLT)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
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