This paper describes the systems developed by the Center for Robust Speech Systems (CRSS), Univ. of Texas - Dallas, for the National Institute of Standards and Technology (NIST) i-vector challenge. Given that the emphasis of this challenge is on utilizing unlabeled development data, our system development focuses on: 1) leveraging the channel variation from unlabeled development data through unsupervised clustering; 2) investigating different classifiers containing complementary information that can be used in fusion; and 3) extracting meta-data information for test and model i-vectors. Our results indicate substantial improvements obtained from incorporating one or more of the aforementioned techniques.