Aiming at the problem that speaker recognition is susceptible to environmental noise, a new voiceprint extraction method was proposed based on the spatial-temporal filtering mechanism of Spectra-Temporal Receptive Field (STRF) of biological auditory cortex neurons. In the method, the quadratic characteristics were extracted from the auditory scale-rate map based on STRF, and the traditional Mel-Frequency Cepstral Coefficient (MFCC) was combined to obtain the voiceprint features with strong tolerance to environmental noise. Using Support Vector Machine (SVM) as feature classifier, the testing results on speech data with different Signal-to-Noise Ratios (SNR) showed that the STRF-based features were more robust to noise than MFCC coefficient, but had lower recognition accuracy; the combined features improved the accuracy of speech recognition and had good robustness to noise. The results verify the effectiveness of the proposed method in speaker recognition under strong noise environment.