Optimal approch for real time continuous speech recognition system

We have developed the 30K word real time continuous speech recognition based on a context dependent Hidden Markov Model (HMM). Here we are using a 30K word language model instead of previously using 20K[15] word speech recognition. It has opened new opportunities for speech recognition innovations. In 20K [15]word speech recognition has been designed with limited vocabulary .i.e., 800 words[9] but in this 30K word language model to be designed by using the high level vocabulary. In this system contains two parts. One is training and second is testing. First different input speech signals will be stored in training kit. Second will give different speech signals for testing, after comparing with training kit it will display final output. Gaussian Mixture Models (GMMs)[3] are used to represent the state of output probability of HMMs.