Cross-domain variation compensation for robust speaker verification

Recent studies have shown that when state-of-the-art probabilistic linear discriminant analysis (PLDA) speaker verification systems are developed with out-domain data, the mismatch between development data and evaluation data significantly degrades speaker verification performance. An unsupervised cross-domain variation compensation (CDVC) approach to compensate the domain mismatch is proposed. This approach is based on the assumption that the inter-domain variability is an additive factor with normal distribution in the i-vector space. The effect of the approach on the domain adaption challenge of the JHU 2013 speaker recognition workshop is tested. Applying the CDVC approach on evaluation i-vectors, the out-domain PLDA system achieves a relative performance improvement of 61.9% in equal error rate.