This paper describes the systems developed by the Center for Robust Speech Systems (CRSS), Univ. of Texas - Dallas, for the National Institute of Standards and Technology (NIST) i-vector challenge. Given that 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 improvements obtained from incorporating one or more of the aforementioned techniques.
Cite as: Liu, G., Hansen, J., Yu, C., Misra, A., Shokouhi, N. (2014) Investigating State-of-the-Art Speaker Verification in the case of Unlabeled Development Data. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 118-122, doi: 10.21437/Odyssey.2014-22
@inproceedings{liu14b_odyssey,
author={Gang Liu and John Hansen and Chengzhu Yu and Abhinav Misra and Navid Shokouhi},
title={{Investigating State-of-the-Art Speaker Verification in the case of Unlabeled Development Data}},
year=2014,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
pages={118--122},
doi={10.21437/Odyssey.2014-22}
}