Wnn Speech Recognition Based On Adsabc Algorithm

Wavelet neural network (WNN) has a highly nonlinear mapping function and a strong adaptive ability, but the WNN algorithm tends to fall into local minimum and converges slowly. The artificial bee colony algorithm (ABC) has a strong global search ability and faster convergence rate. The two algorithms complement each other and have been applied to voice recognition. In this paper, ABC algorithm is improved. A new solution search equation is proposed in the bee mining and the observation bee. Adaptive Double Search is used to solve the problem, so as to improve the convergence speed and convergence accuracy. And we combined with WNN algorithm to form a new algorithm ADSABC-WNN which can not only overcome the shortcomings of WNN algorithm but also save the advantages of both. The experimental results show that compared with the traditional ABC algorithm, the recognition rate improves, and the recognition rate increases by 4. 51% when the vocabulary size is 50. Compared with the wavelet neural network model optimized by other methods, this hybrid model can effectively reduce the recognition time under the noise environment, and can obviously improve the training speed of network and the recognition rate of speech recognition.