A new approach with score-level fusion for the classification of a speaker age and gender.

LECTURERS; AGE; GENDER; TASK performance; GAUSSIAN mixture models; SUPPORT vector machines

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. [ABSTRACT FROM AUTHOR]

Copyright of Computers & Electrical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)