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This study describes systems submitted by the Center for Robust Speech Systems (CRSS) from the University of Texas at Dallas (UTD) to the 2016 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE).We developed 4 UBM and DNN i-vector based speaker recognition systems with alternate data sets and feature representations. Given that the emphasis of the NIST SRE 2016 is on language mismatch between training and enrollment/test data, so-called domain mismatch, in our system development we focused on: (i) utilizing unlabeled in-domain data for centralizing i-vectors to alleviate the domain mismatch; (ii) selecting the proper data sets and optimizing configurations for training LDA/PLDA; (iii) introducing a newly proposed dimension reduction technique which incorporates unlabeled in-domain data before PLDA training; (iv) unsupervised speaker clustering of unlabeled data and using them alone or with previous SREs for PLDA training, and finally (v) score calibration using unlabeled data with “pseudo” speaker labels generated from speaker clustering. NIST evaluations show that our proposed methods were very successful for the given task.