  State-of-the-art speaker recognition systems are trained with a large amount
of human-labeled training data set. Such a training set is usually composed of
various data sources to enhance the modeling capability of models. However, in
practical deployment, unseen condition is almost inevitable. Domain mismatch is
a common problem in real-life applications due to the statistical difference
between the training and testing data sets. To alleviate the degradation caused
by domain mismatch, we propose a new feature-based unsupervised domain
adaptation algorithm. The algorithm we propose is a further optimization based
on the well-known CORrelation ALignment (CORAL), so we call it CORAL++. On the
NIST 2019 Speaker Recognition Evaluation (SRE19), we use SRE18 CTS set as the
development set to verify the effectiveness of CORAL++. With the typical
x-vector/PLDA setup, the CORAL++ outperforms the CORAL by 9.40% relatively on
EER.
