Age estimation based on speech features and support vector machine

Abstract

Age estimation based on human's speech features is an interesting subject in Automatic Speech Recognition (ASR) systems. There are some works in literature on speaker age estimation but it needs more new works especially for Persian speakers. In age estimation, like other speech processing systems, we encounter with two main challenges: finding an appropriate procedure for feature extraction, and selecting a reliable method for pattern classification. In this paper we propose an automatic age estimation system for classification of 6 age groups of various Persian speaker people. Perceptual Linear Predictive (PLP) and Mel-Frequency Cepstral Coefficients (MFCC) are extracted as speech features and SVM is utilized for classification procedure. Furthermore the effects of variations in parameter of kernel function, time of frame length in sampling process, the number of MFCC coefficients, and the order of PLP on system efficiency has been evaluated, and the results has been compared.