This paper presents an effective and robust method for extracting features for speech processing. Based on the time-frequency multiresolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristics of the vocal track and vocal codes, the traditional linear predictive cepstral coefficients (LPCC) of the approximation channel, and the entropy of the detail channel for each decomposition process are calculated. In addition, a hard thresholding technique for each lower resolution is applied to remove interference from noise. Experimental results show that using this mechanism not only effectively reduces the influence of noise, but also improves recognition. Finally, the proposed feature extraction algorithm is evaluated on the MAT telephone speech database for text-independent speaker identification using the Gaussian Mixture Model (GMM) identifier. Some popular existing methods are also evaluated for comparison in this paper. The results show that the proposed method of feature extraction is more effective and robust than other methods. In addition, the performance of our method is very satisfactory even at low SNR.