Performance evaluation of artificial neural networks for isolated Hindi digit recognition with LPC and MFCC

Artificial neural networks (ANN) are one of the most robust classifiers having a long standing history of application for voice recognition. In this paper, a comparative study between two different types of neural networks for isolated Hindi digit recognition has been presented. The two networks, pattern net and feed-forward net have been used for digits classification with multiple combinations of transfer functions and hidden neurons. LPC, MFCC and combinations of both have been used as feature extraction techniques for experiments. The results have been found in favor of pattern net for all the tested cases. A noiseless database of 50 independent speakers has been used for simulation.