A study of appling BPNN to speech recognition system

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 needing the real time response. But using neural network as the identifier, the dimension of input vector will large, it will occupy more memory storage, then will affect the efficiency of calculation. Therefore, in this paper we raise the concept to combine HMM and BPNN, it can reduce the dimension of input vector to decrease the burden of memory storage; on the other hand, it can also raise the calculating efficiency. For resolving a general BP network problem of slow convergence while training, in this paper we raise the concept of using the recognition rate as a factor to judge whether to stop the training procedure or not, which can save more training time and can also get the required recognition rate.