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One particularly difficult challenge for cross-channel speaker verification is the auxiliary microphone task introduced in the 2005 and 2006 NIST Speaker Recognition Evaluations, where training uses telephone speech and verification uses speech from multiple auxiliary microphones. This paper presents two approaches to compensate for the effects of auxiliary microphones on the speech signal. The first compensation method mitigates session effects through Latent Factor Analysis (LFA) and Nuisance Attribute Projection (NAP). The second approach operates directly on the recorded signal with noise reduction techniques. Results are presented that show a reduction in the performance gap between telephone and auxiliary microphone data.