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State-of-the-art speaker recognition systems are trained with a large amountof human-labeled training data set. Such a training set is usually composed ofvarious data sources to enhance the modeling capability of models. However, inpractical deployment, unseen condition is almost inevitable. Domain mismatch isa common problem in real-life applications due to the statistical differencebetween the training and testing data sets. To alleviate the degradation causedby domain mismatch, we propose a new feature-based unsupervised domainadaptation algorithm. The algorithm we propose is a further optimization basedon the well-known CORrelation ALignment (CORAL), so we call it CORAL++. On theNIST 2019 Speaker Recognition Evaluation (SRE19), we use SRE18 CTS set as thedevelopment set to verify the effectiveness of CORAL++. With the typicalx-vector/PLDA setup, the CORAL++ outperforms the CORAL by 9.40% relatively onEER.

The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion