Ensemble majority voting classifier for speech emotion recognition and prediction. Issue 3 (5th August 2014)

• Record Type: Journal Article
• Authors: Anagnostopoulos, Theodoros; Skourlas, Christos
• Editors: Georgios Giannakopoulos and Professor Damianos Sakas, Professor
• Is Part Of: Journal of systems and information technology. Volume 16:Issue 3(2014)
• Page Start: 222
• Page End: 232
• Publication Date: 2014-08-05
• Subjects: Speech emotion recognition -- Affective computing -- Machine learning; Management information systems -- Periodicals; Information storage and retrieval systems -- Periodicals; Information technology -- Periodicals; 004.205
• DOI: 10.1108/JSIT-01-2014-0009
• Languages: English
• ISSNs: 1328-7265

Abstract

Abstract : Purpose: – 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.