In this paper we introduce a novel domain-invariant covariance normalization (DICN) technique to relocate both in-domain and out-domain i-vectors into a third dataset-invariant space, providing an improvement for out-domain PLDA speaker verification with a very small number of unlabelled in-domain adaptation i-vectors. By capturing the dataset variance from a global mean using both development out-domain i-vectors and limited unlabelled in-domain i-vectors, we could obtain domain-invariant representations of PLDA training data. The DICN-compensated out-domain PLDA system is shown to perform as well as in-domain PLDA training with as few as 500 unlabelled in-domain i-vectors for NIST-2010 SRE and 2000 unlabelled in-domain i-vectors for NIST-2008 SRE, and considerable relative improvement over both out-domain and in-domain PLDA development if more are available.
Cite as: Rahman, M.H., Kanagasundaram, A., Dean, D., Sridharan, S. (2015) Dataset-invariant covariance normalization for out-domain PLDA speaker verification. Proc. Interspeech 2015, 1017-1021, doi: 10.21437/Interspeech.2015-276
@inproceedings{rahman15_interspeech,
author={Md. Hafizur Rahman and Ahilan Kanagasundaram and David Dean and Sridha Sridharan},
title={{Dataset-invariant covariance normalization for out-domain PLDA speaker verification}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={1017--1021},
doi={10.21437/Interspeech.2015-276}
}