We analyze the performance of continuous speech recognition of a speaker independent system using Hidden Markov Model and Artificial Neural Network. Modern speech recognition systems use different combinations of the standard techniques over the basic approach to improve performance accuracy. One such combination which has gained more attention is the hybrid model. Our hybrid system for continuous speech recognition consists of a combination of Hidden Markov Model in the front end and the Neural Network with Radial basis function as the back end. The speech recognition process consists of the training phase and the recognition phase. The speech sentences are pre-processed and the features are extracted. The extracted feature vector is clustered into a model database by Hidden Markov Model and is trained by the Radial Basis Function Neural Network. During the recognition phase, the continuous sentence is preprocessed and its feature vector is modelled. This is compared with the database model which contains models stored during the training process. When a match occurs, the model is recognized and the recognition is made for the least error. From the recognized output the word error rate is computed, which is a measure of recognition performance of the hybrid model. The audio files of continuous sentences are taken from the TIMIT database. The performance of our hybrid HMM/RBFNN gives 65% recognition rate.
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