Automatic Urdu Speech Recognition using Hidden Markov Model

In this paper, we present an approach to develop an Automatic Speech Recognition (ASR) system of Urdu isolated words. Our experimentation is based on a medium vocabulary speech corpus of Urdu, consisting of 250 words. We develop our approach using the open source Sphinx toolkit. Using this platform, we extract the Mel Frequency Cepstral Coefficients (MFCC) features and build a Hidden Markov Model to perform recognition task. We report percentage accuracy for two different experiments based on 100 and 250 words respectively. Experimental results suggest that better recognition accuracy has been achieved with this approach, as compared to the previous results reported on this corpus.