1. INTRODUCTION
The benefits of ASR disappear quickly when the training and the testing conditions mismatch greatly in unknown environments. These mismatches are due to three reasons - (I) inter-and intraspeaker variabilities, (II) additive background noises, and (III) microphone and transmission channel interferences [9]. An insufficiency of training data to train the model parameters may also contribute to the acoustic mismatches. These variabilities could severely hamper the performance of ASR to an extent that would make it unacceptable for real-world applications. In robust ASR, the goal is to reduce the effects of such extraneous conditions to bring the recognition performance closer to that experienced in matched testing environments.