In this paper we propose a low-cost robust MFCC feature extraction algorithm which combines noise reduction and voice activity detection (VAD) for automatic speech recognition (ASR) system of embedded applications. To remedy the effect of additive noise a magnitude spectrum subtraction method is used. A VAD is performed to distinguish speech signal from noise signal. It discriminates speech/nonspeech frames by employing an order statistics filter (OSF) on subband spectral entropy. A general RASTA filtering on log Mel filter-bank energy trajectories are applied. Finally, a 26 dimensional feature vector is used in ASR system after feature selection. Experimental results show that the proposed front-end can obtain 30.08% and 62.55% relative improvements on Aurora2 and Aurora3 databases and 29.47% on a Mandarin database compared with the baseline obtained from ETSI standard MFCC front-end.