Abstract: In text-independent speaker verification research, feature mapping can reduce the bias by the channel. In this paper, the subspace of the channel is estimated by the generalized principal component analysis, then the bias of the channel is subtracted from the acoustic feature. The proposed algorithm requires labeled data in the training process but does not need the channel detection in the feature mapping process. In the NIST 2006 SRE 1conv4w-1conv4w corpus, the equal error rate (EER) of the proposed system can be down by 19\% against the baseline Gaussian mixture model (GMM) system.