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In this paper, a session variability subspace projection (SVSP) based model compensation method for speaker verification is proposed. During the training phase the session variability is removed from speaker models by projection, while during the testing phase the session variability in a test utterance is used to compensate speaker models. Finally, the compensated speaker models and UBM are used to recognize the identity of the test utterance. Compared with the conventional GMM-UBM system, the relative equal error rate reduction of SVSP is 16.2% on the NIST 2006 single-side one conversation training, single-side one conversation test.