Plda-based system for text-prompted password speaker verification

Recently we have proposed a new State-GMM-supervector extractor for solving the problem of text-dependent speaker recognition. We demonstrated that segmenting the passphrase into word states for supervector extraction makes it possible to create more accurate statistical models of speech signals and to achieve reduction of EER compared to the best state-of-the-art systems of text-dependent verification for a text-prompted passphrase. In this paper we used a similar approach for creating a text-dependent verification system based on PLDA. The proposed system is easy to implement. Although the PLDA system is inferior to the baseline systems, fusing it with the baseline systems leads to improved quality of text-prompted password speaker verification in comparison to the fusion of baseline systems.