Age and Gender Classification for a Home-Robot Service

Abstract

This paper describes a method to recognize the age and gender of a user on the basis of human speech. Using voice source characteristics of the Mel frequency cepstral coefficients (MFCCs), a Gaussian mixture model (GMM) technique is applied in an effort to discover the age, gender, and other information as regards a user. On the basis of this information, service applications for robots can satisfy users by offering services adaptive to the special needs of specific user groups that may include adults and children as well as females and males. The major aim of this paper is to discover the voice source parameters of age and gender and to classify these two characteristics simultaneously. ETRI-VoiceDB2006 was employed to evaluate the proposed method.