Class dependent LDA optimization using genetic algorithm for robust MFCC extraction

Abstract

Linear Discrimination analysis (LDA) finds transformations that maximizes the between-class scatter and minimizes within-class scatter. In this paper, we propose a method to use class-dependent LDA for speech recognition and MFCC extraction. To this end, we first use logarithm of clean speech Mel filter bank energies (LMFE) of each class then we obtain class-dependent LDA transformation matrix using multidimensional genetic algorithm (MGA) and use this matrix in place of DCT in MFCC feature extraction. The experimental results show that proposed speech recognition and optimization methods using class-dependent LDA, achieves to a significant isolated word recognition rate on Aurora2 database. © 2008 Springer-Verlag.