This thesis studies the speech feature extracting and feature compensation in speech recognition. Several speech features are selected for combinations. The best one is cascading Linear Prediction Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficient (MFCC). The MFCCs used here are obtained by utilizing a Gaussian Mel-Frequency band instead of using a triangular filter bank. And by experiments, it is found that the best combination ratio of LPCC and MFCC is 1:1. The thesis also showed that further improved performance is possible if Cepstral Mean and Variance Normalization (CMVN) is added.