An Improved fastICA Based on the Negative Entropy for Voiceprint Identification

Voiceprint Identification is a technology which can determine the identity of the speaker based on the voice. Now a good recognition performance can be obtained in the lab environment, but in the real environment, it is susceptible to the interference of ambient noise and a variety of channels, and the recognition rate declines. In order to improve the recognition rate, it´s introduced the traditional voice denoising algorithms in the signal space, which can be applied to improve the signal to noise ratio(SNR). But when the noise is strong, the effect becomes minor, so the denoising method based on independent component analysis(ICA) is discussed in this paper, and experiments show that this denoising method improves SNR significantly.