Domain adaptation using maximum likelihood linear transformation for PLDA-based speaker verification

While i-vector-PLDA frameworks employing huge amounts of development data have achieved significant success in speaker recognition, it is infeasible to collect a sufficiently large amount of data for every real application. This paper proposes a method to perform supervised domain adaptation of PLDA in i-vector-based speaker recognition systems with available resource-rich mismatched data and small amounts of matched data, under two assumptions: (1) between-speaker and within-speaker covariances depend on domains; (2) features in one domain can be transformed into another domain by means of an affine transformation. Maximum likelihood linear transformation (MLLT) is used to infer the relationship between the datasets of two domains in training PLDA. The proposed method improves performance over that achieved without adaptation. Using a score fusion technique, it outperforms a conventional method based on linear combination.