In order to improve the accuracy of speaker recognition, multiple feature parameters should be adopted simultaneously. For the problem that each dimension comprehensive feature parameter has the different influence on the identification result, and treating them equally may not be the optimal solution, a feature parameter extraction method based on Fisher criterion combined with Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Mel Frequency Cepstrum Coefficient (LPMFCC) and Teager Energy Operators Cepstrum Coefficient (TEOCC) was proposed. Firstly, parameters of MFCC, LPMFCC and TEOCC from speech signals were extracted, and then the Fisher ratio of each dimension of MFCC and LPMFCC parameters was calculated, six components were selected respectively by using Fisher standard to combine with TEOCC parameter into a mixture feature which was used to realize speaker recognition on the TIMIT acoustic-phonetic continuous speech corpus and NOISEX-92 noise library. The simulation results show that the average recognition rate of the proposed method by using Gauss Mixed Model (GMM) and Back Propagation (BP) neural network compared with MFCC, LPMFCC, MFCC+LPMFCC, parameter extraction method for MFCC based on Fisher criterion and the feature extraction method based on Principal Component Analysis (PCA) is increased by 21.65 percentage points, 18.39 percentage points, 15.61 percentage points, 15.01 percentage points, 22.70 percentage points in the pure voice database, and by 15.15 percentage points, 10.81 percentage points, 8.69 percentage points, 7.64 percentage points, 17.76 percentage points in 30 dB noise environments. The results show that the mixture feature can improve the recognition rate effectively and has better robustness.