  The NIST Speaker Recognition Evaluation - Conversational Telephone Speech
(CTS) challenge 2019 was an open evaluation for the task of speaker
verification in challenging conditions. In this paper, we provide a detailed
account of the LEAP SRE system submitted to the CTS challenge focusing on the
novel components in the back-end system modeling. All the systems used the
time-delay neural network (TDNN) based x-vector embeddings. The x-vector system
in our SRE19 submission used a large pool of training speakers (about 14k
speakers). Following the x-vector extraction, we explored a neural network
approach to backend score computation that was optimized for a speaker
verification cost. The system combination of generative and neural PLDA models
resulted in significant improvements for the SRE evaluation dataset. We also
found additional gains for the SRE systems based on score normalization and
calibration. Subsequent to the evaluations, we have performed a detailed
analysis of the submitted systems. The analysis revealed the incremental gains
obtained for different training dataset combinations as well as the modeling
methods.
