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Title: A new approach with score-level fusion for the classification of a speaker age and gender
Authors: Yucesoy, Ergun
Nabiyev, Vasif V.
Ordu Üniversitesi
Keywords: Age and gender recognition, Spectral features, Prosodic features, Score-level fusion, Gaussian Mixture Model, Support Vector Machines
Issue Date: 2016
Citation: Yücesoy, E., Nabiyev, VV. (2016). A new approach with score-level fusion for the classification of a speaker age and gender. Comput. Electr. Eng., 53, 29-39.
Abstract: In this study a new approach for classifying speakers according to their age and genders is proposed. This approach is composed of score-level fusion of seven sub-systems. In this fused system, which provides improved performance in three classification categories (age, gender and age & gender), spectral and prosodic features extracted from short-duration phone conversations are used with Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and GMM supervector-based SVM classifiers. Also, by examining individual and various combinations of each system, the effect of feature types and classification methods on performance is investigated. With the proposed system, classification success rates are obtained 90.4%, 54.1%, and 53.5% in gender, age and age & gender categories respectively. (C) 2016 Elsevier Ltd. All rights reserved.
Description: WoS Categories: Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic
Web of Science Index: Science Citation Index Expanded (SCI-EXPANDED)
Research Areas: Computer Science; Engineering
ISSN: 0045-7906
Appears in Collections:Makale Koleksiyonu

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