The state-of-the-art i-vector based probabilistic linear discriminant analysis (PLDA) trained on non-target (or out-domain) data significantly affects the speaker verification performance due to the domain mismatch between training and evaluation data. To improve the speaker verification performance, sufficient amount of domain mismatch compensated out-domain data must be used to train the PLDA models successfully. In this paper, we propose a domain mismatch modeling (DMM) technique using maximum-a-posteriori (MAP) estimation to model and compensate the domain variability from the out-domain training i-vectors. From our experimental results, we found that the DMM technique can achieve at least a 24% improvement in EER over an out-domain only baseline when speaker labels are available. Further improvement of 3% is obtained when combining DMM with domain-invariant covariance normalization (DICN) approach. The DMM/DICN combined technique is shown to perform better than in-domain PLDA system with only 200 labeled speakers or 2,000 unlabeled i-vectors.
Cite as: Rahman, M.H., Himawan, I., Dean, D., Sridharan, S. (2017) Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification. Proc. Interspeech 2017, 1581-1585, doi: 10.21437/Interspeech.2017-668
@inproceedings{rahman17_interspeech,
author={Md. Hafizur Rahman and Ivan Himawan and David Dean and Sridha Sridharan},
title={{Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1581--1585},
doi={10.21437/Interspeech.2017-668}
}