On comparing and combining intra-speaker variability compensation and unsupervised model adaptation in speaker verification

In this paper an unsupervised intra-speaker variability compensation method, ISVC, and unsupervised model adaptation are tested to address the problem of limited enrolling data in text-dependent speaker verification. In contrast to model adaptation methods, ISVC is memoryless with respect to previous verification attempts. As shown here, unsupervised model adaptation can lead to substantial improvements in EER but is highly dependent on the sequence of client/impostor verification events. In adverse scenarios, unsupervised model adaptation might even provide reductions in verification accuracy when compared with the baseline system. In those cases, ISVC may outperform adaptation schemes. It is worth emphasizing that ISVC and unsupervised model adaptation are compatible and the combination of both methods always improves the performance of model adaptation. The combination of both schemes can lead to improvements in EER as high as 34%.