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dc.contributorEscalera Guerrero, Sergio
dc.contributorJacques Junior, Julio
dc.contributorPalmero Cantariño, Cristina
dc.contributor.authorVidal Lucero, Ítalo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2021-01-29T08:00:48Z
dc.date.available2021-01-29T08:00:48Z
dc.date.issued2020-07-01
dc.identifier.urihttp://hdl.handle.net/2117/336189
dc.description.abstractThe purpose of this project is to design a general system for emotion recognition through social signals in dyadic using deep learning methods using raw data from audio, video and text transcriptions from publicly available database records. The automatic emotion recognition problem has increased the attention in the scientific community considering the multi applications for emotion detection but also to design more accurate and complex empathic machines. During this project are proposed alternatives for utterance representation of multi-modal data generated from text, audio and video, in order to improve the state of the art system for emotion recognition based on deep learning networks. The proposed framework is based in IEMOCAP database but it has a general scope for any multi-modal database. The performance of this system outperforms the state of the art method and delivers an informative analysis concerning the utterance representation quality. Finally, the conclusions of this work are exposed along with potential future lines of work related to emotion recognition systems and emotion representations.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshMachine learning
dc.subject.otheremotion recognition
dc.subject.otherrecurrent neural networks
dc.subject.otherfeature extraction
dc.subject.othermulti-modal database
dc.subject.otherdyadic scenario
dc.titleDeep regression of social signals in Dyadic Scenarios
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacAprenentatge automàtic
dc.identifier.slug148691
dc.rights.accessOpen Access
dc.date.updated2020-09-21T06:53:11Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)
dc.contributor.covenanteeUniversitat de Barcelona
dc.contributor.covenanteeUniversitat Rovira i Virgili


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