Abstract
Aiming at the fact that common pattern recognition algorithms such as LPCC and MFCC have flaws in voice-print recognition, the paper put forward a new algorithm, which employed wavelet analysis, BP neural network and niche genetic algorithm. Firstly, the algorithm extracted time-domain and frequency-domain characteristic variation of voice signal by wavelet transform, secondly, trained the neural network niche genetic algorithm, which solved the problem of local minimum value caused by normal multi-layers neural network, lastly, took the wavelet transform variations as the training data of the optimization neural network. Simulation experiment was carried out. The results indicate the algorithm in this paper is superior to current recognition algorithms, it has fast recognition velocity, high recognition rate, low fault rate and strong robustness with different voice-makers, besides, it can automatically correct errors.
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