In order to improve the performance of speaker recognition system in noisy environment

this paper presents an auditory feature extraction algorithm.It used adaptive compression Gammachirp filter banks to simulate the auditory characteristics of human cochlea

and the input speech signal was sub-band filtered in the frequency-domain.After logarithmic transformation

it can get the logarithmic sub-band energy as the auditory feature parameter.It respectively used discrete cosine transform and kernel principal component analysis method to transform the auditory feature and get the two new auditory features

which not only can reduce the dimension of the feature parameters

but also can improve the robustness and personality expression of feature parameters.The experimental results show that speaker recognition system with the new auditory feature parameters can get the better results in the robustness and recognition performance than Mel cepstral coefficients and auditory feature parameters based on Gammatone filter banks.