Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification

The state-of-the-art i-vector based probabilistic linear discriminant analysis (PLDA) trained on non-target (or outdomain) data significantly affects the speaker verification performance due to the domain mismatch between training and evaluation data. To improve the speaker verification performance, sufficient amount of domain mismatch compensated out-domain data must be used to train the PLDA models successfully. In this paper, we propose a domain mismatch modeling (DMM) technique using maximum-a-posteriori (MAP) estimation to model and compensate the domain variability from the out-domain training i-vectors. From our experimental results, we found that the DMM technique can achieve at least a 24% improvement in EER over an out-domain only baseline when speaker labels are available. Further improvement of 3% is obtained when combining DMM with domain-invariant covariance normalization (DICN) approach. The DMM/DICN combined technique is shown to perform better than in-domain PLDA system with only 200 labeled speakers or 2,000 unlabeled i-vectors.