{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:39Z","timestamp":1754156079843,"version":"3.41.2"},"reference-count":29,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2014,8,5]],"date-time":"2014-08-05T00:00:00Z","timestamp":1407196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jsit-01-2014-0009","type":"journal-article","created":{"date-parts":[[2014,8,29]],"date-time":"2014-08-29T03:32:37Z","timestamp":1409283157000},"page":"222-232","source":"Crossref","is-referenced-by-count":6,"title":["Ensemble majority voting classifier for speech emotion recognition and prediction"],"prefix":"10.1108","volume":"16","author":[{"given":"Theodoros","family":"Anagnostopoulos","sequence":"first","affiliation":[]},{"given":"Christos","family":"Skourlas","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2020122920223112000_b1","unstructured":"Alpaydin, E. (2010), Introduction to Machine Learning, 2nd ed., The MIT Press."},{"key":"key2020122920223112000_b2","doi-asserted-by":"crossref","unstructured":"Bitouk, D. , Verma, R. and Nenkova, A. 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