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Spanish MEACorpus 2023: a multimodal speech–text corpus for emotion analysis in Spanish from natural environments

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dc.contributor.authorPan, Ronghao
dc.contributor.authorGarcía Díaz, José Antonio
dc.contributor.authorRodríguez García, Miguel Ángel
dc.contributor.authorValencia García, Rafael
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-14T11:46:13Z
dc.date.available2026-01-14T11:46:13Z
dc.date.copyright© 2024 Los autores
dc.date.issued2024-08
dc.description.abstractIn human–computer interaction, emotion recognition provides a deeper understanding of the user’s emotions, enabling empathetic and effective responses based on the user’s emotional state. While deep learning models have improved emotion recognition solutions, it is still an active area of research. One important limitation is that most emotion recognition systems use only text as input, ignoring features such as voice intonation. Another limitation is the limited number of datasets available for multimodal emotion recognition. In addition, most published datasets contain emotions that are simulated by professionals and produce limited results in real-world scenarios. In other languages, such as Spanish, hardly any datasets are available. Therefore, our contributions to emotion recognition are as follows. First, we compile and annotate a new corpus for multimodal emotion recognition in Spanish (Spanish MEACorpus 2023), which contains 13.16 h of speech divided into 5129 segments labeled by considering Ekman’s six basic emotions. The dataset is extracted from YouTube videos in natural environments. Second, we explore several deep learning models for emotion recognition using text- and audio-based features. Third, we evaluate different multimodal techniques to build a multimodal recognition system that improves the results of unimodal models, achieving a Macro F1-score of 87.745%, using late fusion with concatenation strategy approach.
dc.formatapplication/pdf
dc.format.extent13
dc.identifier.citationComputer Standards & Interfaces, 2024, Vol. 90 : 103856
dc.identifier.doihttps://doi.org/10.1016/j.csi.2024.103856
dc.identifier.eissn1872-7018
dc.identifier.issn0920-5489
dc.identifier.urihttp://hdl.handle.net/10201/186889
dc.languageeng
dc.publisherElsevier
dc.relationThis work is part of the research projects LaTe4PoliticES (PID2022- 138099OB-I00) funded by MICIU/AEI/10.13039/501100011033 and the European Regional Development Fund (ERDF)-a way to make Europe and LT-SWM (TED2021-131167B-I00) funded by MICIU/AEI/10.13039/ 501100011033 and by the European Union NextGenerationEU/PRTR. In addition, this work has been also funded by ‘‘Programa para la Recualificación del Sistema Universitario Español 2021-2023’’, and the Community of Madrid, through the Young Researchers R+D Project. Ref. M2173 – SGTRS (co-funded by Rey Juan Carlos University) and PEJD-2019-PRE/TIC-16151. Mr. Ronghao Pan is supported by the Programa Investigo grant, funded by the Region of Murcia, the Spanish Ministry of Labour and Social Economy and the European Union - NextGenerationEU under the "Plan de Recuperación, Transformación y Resiliencia (PRTR)".
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0920548924000254?via%3Dihub
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMultimodal emotion analysis
dc.subjectDeep learning
dc.subjectTransformers
dc.subjectText classification
dc.subjectNatural language processing
dc.subjectSpeech emotion analysis
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleSpanish MEACorpus 2023: a multimodal speech–text corpus for emotion analysis in Spanish from natural environments
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication14ca7de1-eef1-42b4-9649-b765516ea4f3
relation.isAuthorOfPublicationab591422-699c-4535-8e8f-fd09f0e90ec2
relation.isAuthorOfPublication.latestForDiscovery14ca7de1-eef1-42b4-9649-b765516ea4f3
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