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Neural network has the advantages of high tolerance of error and ability of parallelism calculation . When applying to the real time speech recognition system, through one time calculation and can get the recognition result immediately that is different from other methods like VQ, DTW, HMM. Those methods need to build models one by one and the same as to recognize, and it is inconvenient on the situation...
This paper uses BP neural network for modeling and recognition in the ASR (automatic speech recognition system) to get a high performance. But it still has some disadvantages, one of which is that it needs to construct a high dimension of input vector, so it will waste a lot of memory storage and spend much time in computing. In this paper we present a new method to combine HMM and BPNN to decrease...
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