Abstract

Automatic speech recognition (ASR) can be formulated as statistical pattern classification problem. In this approach, normally short term features are derived from the speech signal at front-end and then evaluated at back-end using the hidden Markov models (HMMs) or artificial neural networks. In this paper, we present a novel approach by using multilayer perceptrons optimized with the help of genetic algorithm. A combination of both short term and long temporal context features has been used as a sequence of acoustic feature vectors. Experimental result shows significant improvement by using the proposed framework for spoken Hindi digit recognition in general field conditions as well as in noisy environment.

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