DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorSkourlas, Christos-
dc.date.accessioned2024-07-05T07:46:27Z-
dc.date.available2024-07-05T07:46:27Z-
dc.date.issued2014-08-05-
dc.identifierscopus-84927513265-
dc.identifier.issn1758-8847-
dc.identifier.issn1328-7265-
dc.identifier.other84927513265-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2675-
dc.description.abstractPurpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Systems and Information Technologyen_US
dc.subjectAffective computingen_US
dc.subjectMachine learningen_US
dc.subjectSpeech emotion recognitionen_US
dc.titleEnsemble majority voting classifier for speech emotion recognition and predictionen_US
dc.typeArticleen_US
dc.identifier.doi10.1108/JSIT-01-2014-0009en_US
dc.identifier.scopus2-s2.0-84927513265-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume16en_US
dc.relation.issue3en_US
dc.identifier.spage222en_US
dc.identifier.epage232en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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