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In this study, we present systems submitted by the Center for Robust Speech Systems (CRSS) from UTDallas to NIST SRE 2018 (SRE18). Three alternative front-end speaker embedding frameworks are investigated, that includes: (i) i-vector, (ii) x-vector, (iii) and a modified triplet speaker embedding system (t-vector). Similar to the previous SRE, language mismatch between training and enrollment/test data, the so-called domain mismatch, remains as a major challenge in this evaluation. In addition, SRE18 also introduces a small portion of audio from an unstructured video corpus in which speaker detection/diarization is supposedly needed to be effectively integrated into speaker recognition for system robustness. In our system development, we focused on: (i) building novel deep neural network based speaker discriminative embedding systems as utterance level feature representations, (ii) exploring alternative dimension reduction methods, back-end classifiers, score normalization techniques which can incorporate unlabeled in-domain data for domain adaptation, (iii) finding an improved data set configurations for the speaker embedding network, LDA/PLDA, and score calibration training (v) and finally, investigating effective score calibration and fusion strategies. The final resulting systems are shown to be both complementary and effective in achieving overall improved speaker recognition performance.

UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation