Abstract: Speech recognition is an important subject in the field of human computer interaction pattern recognition.A speech recognition system based on deep neural network was constructed in this paper.The model was trained without supervision by using the method of anti-chirp contrast divergence and anti-chirp least squares error.The model optimization was carried out using the average value normalization. The fitting degree of the network to the training set is improved and the error rate of speech recognition was reduced.The system used the multi-condition activation function for the model optimization,then the error rate of speech recognition without noise and noise measurement was further reduced.So the system can reduce the over fitting phenomenon.The model was reduced by using the method of singular value decomposition and reconstruction.Experimental results show that the system can greatly reduce the complexity of the system without affecting the error rate of speech recognition.

Key words: Pattern recognition,Deep neural network,Speech recognition,Hidden markov model(HMM),Model reconstruction