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 out- domain) 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 model- ing (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 base- line 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.