Continuous Hindi speech recognition using Gaussian mixture HMM

Automatic speech recognition (ASR) has been extensively studied during the past few decades. Today, most of the ASR system based on statistical modelling, and HMM is the most popular one among them. Now days, performance of ASR become one of the major bottleneck for its practical use. Literature of ASR shows that optimal performance could be achieved by using Gaussian mixture hidden Markov model (HMM) but choice of Gaussian mixture is arbitrary with little justification. If we use the different number of Gaussian Mixture then we get different results. In this paper, we compare the performance of continuous Hindi speech recognition by using different number of Gaussian mixture. The aim of this paper is to investigate the optimal number of Gaussian mixture that exhibits maximum accuracy in the context of Hindi speech recognition. HMM toolkit HTK 3.4.1 is used for the implementation of this system, in which Mel frequency cepstral coefficient (MFCC) is used as a feature extraction technique. The experimental results show that the maximum performance of the proposed system is achieved when we use four component Gaussian mixtures HMM model.