Automatic Identification of Emotional Information in Spanish TV Debates and Human-Machine Interactions
dc.contributor.author | De Velasco Vázquez, Mikel | |
dc.contributor.author | Justo Blanco, Raquel | |
dc.contributor.author | Torres Barañano, María Inés | |
dc.date.accessioned | 2022-03-02T09:02:38Z | |
dc.date.available | 2022-03-02T09:02:38Z | |
dc.date.issued | 2022-02-11 | |
dc.identifier.citation | Applied Sciences 12(4) : (2022) // Article ID 1902 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10810/55636 | |
dc.description.abstract | Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score. | es_ES |
dc.description.sponsorship | The research presented in this paper was conducted as part of the AMIC and EMPATHIC projects, which received funding from the Spanish Minister of Science under grants TIN2017-85854-C4-3-R and PDC2021-120846-C43 and from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 769872. The first author also received a PhD scholarship from the University of the Basque Country UPV/EHU, PIF17/310. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/TIN2017-85854-C4-3-R | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PDC2021-120846-C43 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/769872 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | speech processing | es_ES |
dc.subject | emotion detection | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | behavioral analysis | es_ES |
dc.subject | human–machine and human–human interaction | es_ES |
dc.title | Automatic Identification of Emotional Information in Spanish TV Debates and Human-Machine Interactions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-02-24T14:50:20Z | |
dc.rights.holder | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/12/4/1902/htm | es_ES |
dc.identifier.doi | 10.3390/app12041902 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Electricidad y electrónica | |
dc.departamentoeu | Elektrizitatea eta elektronika |
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Except where otherwise noted, this item's license is described as 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).