IITG- Indigo Submissions for NIST 2018 Speaker Recognition Evaluation and Post-Challenge Improvements

Abstract

This paper describes the submissions of team Indigo at Indian Institute of Technology Guwahati (IITG) to the NIST 2018 Speaker Recognition Evaluation (SRE18) challenge. These speaker verification (SV) systems are developed for the fixed training condition task in SRE18. The evaluation data in SRE18 is derived from two corpora: (i) Call My Net 2 (CMN2), and (ii) Video Annotation for Speech Technology (VAST). The VAST set is obtained by extracting audio from video having high musical/noisy background. Thus, it helps in assessing the robustness of the SV systems. A number of sub-systems are developed which differ in front-end modeling paradigms, backend classifiers, and suppression of repeating pattern in the data. The fusion of sub-systems is submitted as the primary system which achieved actual detection cost function (actDCF) and equal error rate (EER) of 0.77 and 13.79 %, respectively, on the SRE18 evaluation data. Post-challenge efforts include the domain adaptation of the scores and the voice activity detection using deep neural network. With these enhancements, for the VAST trials, the best single sub-system achieves the relative reductions of 38.4% and 11.6% in actDCF and EER, respectively.