Abstract

In this paper, an unsupervised intra-speaker variability compensation (ISVC) method based on Gestalt is proposed to address the problem of limited enrolling data and noise robustness in text-dependent speaker verification (SV). Experiments with two databases show that: ISVC can lead to reductions in EER as high as 20% or 40% and ISCV provides reductions in the integral below the ROC curve between 30% and 60%. Also, the observed improvements are independent of the number of enrolling utterances. 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, such as massive impostor attacks and verification from alternated telephone line, unsupervised model adaptation might even provide reductions in verification accuracy when compared with the baseline system. In those cases, ISVC can even 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%. Due to the restrictions of commercially available databases for text-dependent SV research, the results presented here are based on local databases in Spanish. By doing so, the visibility of research in Iberian Languages is highlighted. © 2007 Elsevier B.V. All rights reserved.