This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous word recognition on a Japanese speech corpus and proposes a method, the Bias Removal Method (BRM), that reduces the distortion between feature vector and VQ codebook. Experimental results show that using non-quantized features in acoustic model training procedure can improve the recognition performance of DSR front-end features and that the proposed method can improve recognition performances of DSR front-end feature.