Abstract: In order to solve the problem that speaker recognition rate is low under noisy environment, a speaker recognition feature based on regularized linear predictive power spectrum is proposed. The method uses linear prediction analysis and regularization of speech signal to get speech spectral envelope and then to get logarithmic sub-band energy through the Gammatone filter group, and finally uses discrete cosine transform to compute feature parameters to get a kind of speaker recognition feature named regularized linear predicted Gammatone filter cepstral coefficients (RLP-GFCC). The simulation results show that the recognition rate of the system is significantly improved in comparison with the systems of traditional feature MFCC and GFCC under noisy environment, and the robustness of the system to noise environment is improved.