Zero-crossings with peak amplitudes (ZCPA) model motivated by human auditory periphery is simple com- pared with other auditory models, but powerful speech analysis tool for robust speech recognition in noisy environments. In this paper, improvement in recog- nition rate of ZCPA model is addressed by incorpo- rating time-derivative features with several different time-derivative window lengths. Experimental results show that ZCPA has relatively higher sensitivity to derivative window length than conventional feature extraction algorithms. Also, experimental compar- isons with several front-ends including some auditory- like schemes in real-world noisy environments demon- strate the robustness of ZCPA model. ZCPA model shows superior performance compared with other front- ends especially in noisy condition corrupted by white Gaussian noise.