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In this paper, we introduce the residual space into the Total Variability Modeling by assuming that the speaker super-vectors are not totally contained in a linear subspace of low dimension. Thus the feature reduction carried out by Probabilistic Principal Component Analysis (PPCA) leads to information loss including information of speaker as well as channel. We add the residual factor to restore the missing speaker information which is lost during the PPCA process. To utilize the recovered information effectively, we propose two fusion methods that combine the principal components with the residual factor. We compare the fusion results that are obtained with direct scoring and Support Vector Machines for classification, respectively. The experiments on NIST SRE 2006 show that the performance can be improved consistently by involving the residual factor, e.g. the best result achieves 6% relative improvement on Equal Error Rate(EER) compared to the baseline system.