Application of Orthogonal Least Square (OLS) for selection of Mel Frequency Cepstrum Coefficients for classification of spoken letters using MLP classifier

This paper describes an application of the Orthogonal Least Squares (OLS) algorithm for feature selection of spoken letters. Traditionally used for system identification purposes, the OLS method was used to select important Mel-Frequency Cepstrum Coefficients (MFCC) for classification of two spoken letters - `A´ and `S´ using Multi-Layer Perceptron (MLP) neural network. We evaluated several network structures and parameters to determine the best performance in terms of accuracy and speed. The result found that OLS is an effective feature selection method, with the best classification performance of 85% with 6 hidden units.