Investigating State-of-the-Art Speaker Verification in the case of Unlabeled Development Data
Gang Liu, John H. L. Hansen, Chengzhu Yu, Abhinav Misra, Navid Shokouhi · 2014
In this study, we describe the systems developed by the Center for Robust Speech Systems (CRSS), Univ. of Texas - Dallas, for the NIST i-vector challenge. Given the emphasis of this challenge is on utilizing unlabeled development data, our system development focuses on: 1) leveraging the channel variation from unlabeled development data through unsupervised clustering; 2) investigating different classifiers containing complementary information that can be used in fusion; and 3) extracting meta-data information for test and model i-vectors. Our results indicate substantial improvement in performance by incorporating one or more of the aforementioned techniques. Index Terms: i-Vector challenge, UBS-SVM, PLDA, WCCN