  Automatic Speech Recognition involves mainly two steps; feature extraction
and classification . Mel Frequency Cepstral Coefficient is used as one of the
prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC
coefficients is used as the feature vector in the classification step. But the
question is whether the same or improved classification accuracy can be
achieved by using a subset of 12 MFCC as feature vector. In this paper,
Fisher's ratio technique is used for selecting a subset of 12 MFCC coefficients
that contribute more in discriminating a pattern. The selected coefficients are
used in classification with Hidden Markov Model algorithm. The classification
accuracies that we get by using 12 coefficients and by using the selected
coefficients are compared.
