Competitive international language recognition evaluations have been hosted by NIST for over two decades. This paper describes the MIT Lincoln Laboratory (MITLL) and Johns Hopkins University (JHU) submission for the recent 2017 NIST language recognition evaluation (LRE17) [1]. The MITLL/JHU LRE17 submission represents a collaboration between researchers at MITLL and JHU with multiple sub-systems reflecting a range of language recognition technologies including traditional MFCC/SDC i-vector systems, deep neural network (DNN) bottleneck feature based i-vector systems, state-of-the-art DNN x-vector systems and a sparse coding system. Each sub-systems uses the same backend processing for domain adaptation and score calibration. Multiple sub-systems were fused using a simple logistic regression ([2]) to create system combinations. The MITLL/JHU submissions were selected based on the top ranking combinations of up to 5 sub-systems using development data provided by NIST. The MITLL/JHU primary submitted systems attained a Cavg of 0.181 and 0.163 for the fixed and open conditions respectively. Post evaluation analysis revealed the importance of carefully partitioning for the development data, using augmented training data and using a condition dependent backend. Addressing these issues - including retraining the x-vector system with augmented data - yielded gains in performance of over 17%: a Cavg of 0.149 for the fixed condition and 0.132 for the open condition.