  State-of-the-art speaker recognition systems comprise an x-vector (or
i-vector) speaker embedding front-end followed by a probabilistic linear
discriminant analysis (PLDA) backend. The effectiveness of these components
relies on the availability of a large collection of labeled training data. In
practice, it is common that the domains (e.g., language, demographic) in which
the system are deployed differs from that we trained the system. To close the
gap due to the domain mismatch, we propose an unsupervised PLDA adaptation
algorithm to learn from a small amount of unlabeled in-domain data. The
proposed method was inspired by a prior work on feature-based domain adaptation
technique known as the correlation alignment (CORAL). We refer to the
model-based adaptation technique proposed in this paper as CORAL+. The efficacy
of the proposed technique is experimentally validated on the recent NIST 2016
and 2018 Speaker Recognition Evaluation (SRE'16, SRE'18) datasets.
