SUT Submission for NIST 2016 Speaker Recognition Evaluation: Description and Analysis

SUT Submission for NIST 2016 Speaker Recognition Evaluation: Description and Analysis

In this paper, the most recent Sharif University of Technology (SUT) speaker recognition system developed for NIST 2016 Speaker Recognition Evaluation (SRE) is described. The major challenge in this evaluation is the language mismatch between training and evaluation data. The submission is related to the fixed condition of NIST SRE 2016 and features a full description of the database and the systems which were used during the evaluation. Most of the systems are developed in the context of the i-vector framework. Preconditioning the i-vectors, score normalization methods and the classifier used are discussed. The performance of the systems on the development and evaluation parts of the NIST 2016 SRE16 dataset are reported and analyzed. The best obtained minimum and actual DCF are 0.666 and 0.740, respectively. This is achieved by score fusion of several systems and using different methods for mismatch compensation.