dc.contributor.author | De Velasco Vázquez, Mikel ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Justo Blanco, Raquel ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | López Zorrilla, Asier ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Torres Barañano, María Inés ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.date.accessioned | 2020-05-27T17:57:43Z | |
dc.date.available | 2020-05-27T17:57:43Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Naples, Italy, 2019 : 289-294 (2019) | es_ES |
dc.identifier.isbn | 978-1-7281-4793-2 | |
dc.identifier.issn | 2380-7350 | |
dc.identifier.uri | http://hdl.handle.net/10810/43562 | |
dc.description | Accepted paper | es_ES |
dc.description.abstract | Decoding emotional states from multimodal signals is an increasingly active domain, within the framework of affective computing, which aims to a better understanding of Human-Human Communication as well as to improve Human- Computer Interaction. But the automatic recognition of sponta- neous emotions from speech is a very complex task due to the lack of a certainty of the speaker states as well as to the difficulty to identify a variety of emotions in real scenarios.
In this work we explore the extent to which emotional states can be decoded from speech signals extracted from TV political debates. The labelling procedure was supported by perception experiments where only a small set of emotions has been identified. In addition, some scaled judgements of valence, arousal and dominance were also provided. In this framework the paper shows meaningful comparisons between both, the dimensional and the categorical models of emotions, which is a new con- tribution when dealing with spontaneous emotions. To this end Support Vector Machines (SVM) as well as Feedforward Neural Networks (FNN) have been proposed to develop classifiers and predictors. The experimental evaluation over a Spanish corpus has shown the ability of both models to be identified in speech segments by the proposed artificial systems. | es_ES |
dc.description.sponsorship | This work has been partially funded by the Spanish Government under grant TIN2017-85854-C4-3-R (AEI/FEDER,UE) and conducted in the project EMPATHIC (Grant n769872) funded by the European Union’s H2020 research andinnovation program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/769872 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TIN2017-85854-C4-3-R (AEI/FEDER,UE) | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | speech processing | es_ES |
dc.subject | emotion detection from Speech | es_ES |
dc.subject | human-AI | es_ES |
dc.subject | affective computing | es_ES |
dc.title | Can Spontaneous Emotions be Detected from Speech on TV Political Debates? | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.holder | © 2019 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.” | es_ES |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9089948 | es_ES |
dc.identifier.doi | 10.1109/CogInfoCom47531.2019.9089948 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Electricidad y electrónica | es_ES |
dc.departamentoeu | Elektrizitatea eta elektronika | es_ES |