  This paper proposes a generalized framework for domain adaptation of
Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It
not only includes several existing supervised and unsupervised domain
adaptation methods but also makes possible more flexible usage of available
data in different domains. In particular, we introduce here the two new
techniques described below. (1) Correlation-alignment-based interpolation and
(2) covariance regularization. The proposed correlation-alignment-based
interpolation method decreases minCprimary up to 30.5% as compared with that
from an out-of-domain PLDA model before adaptation, and minCprimary is also
5.5% lower than with a conventional linear interpolation method with optimal
interpolation weights. Further, the proposed regularization technique ensures
robustness in interpolations w.r.t. varying interpolation weights, which in
practice is essential.
