On the security of HMM-based speaker verification systems against imposture using synthetic speech

For speaker verification systems, security against imposture is one of the most important problems, and many approaches to reducing false acceptance of impostors as well as false rejection of clients have been investigated. On the other hand, imposture using synthetic speech has not been considered. In this paper, we investigate imposture against speaker verification systems using synthetic speech. We use an HMM-based text-prompted speaker verification system with a false acceptance rate of 0% for human impostors as a reference system, and adopt a trainable HMM-based speech synthesis system for imposture. Experimental results show that false acceptance rates for synthetic speech reached over 70% by training the synthesis system using only 1 sentence from each customer, and current security of HMM-based speaker verification systems against synthetic speech is inadequate.