A novel computational auditory model which simulates the forward-masking mechanism of auditory nerve discharge is presented. Both features based on the model are extracted: FMFRC (forward masking firing-rate cepstrum) and FMSRC (forward masking synchronized rate cepstrum).Isolated-word speech recognition and text-dependent speaker identification experiments based on TI46 are performed. The results show that the new features based on the forward masking model is far more robust than MFCC (mei-frequency cepstrum coefficients) and the performance will be improved compared to the features without such dynamic property. Moreover, the model and the feature extraction method based on it are feasible in practice and promising in robust speech recognition and speaker identification.