Feature extraction using fusion MFCC for continuous marathi speech recognition

This paper presents the performance of feature extraction techniques for speech recognition, for the classification of speech represented by a particular continuous sentence model. The goal of this study is to present independent as well as comparative performances of popular appearance based feature extraction techniques i.e. Linear Discriminative Analysed and Mel Frequency Cestrum Coefficient. Mel Frequency Cepstrum Coefficient (MFCC) helps us in extracting feature where as linear discriminant analysis (LDA) is used for reducing dimension of extracted feature. We experimented MFCC feature extraction individually and proposed a Fusion of MCCC and LDA for feature extraction.