TEXT-DEPENDENT GMM-JFA SYSTEM FOR PASSWORD BASED SPEAKER VERIFICATION

We propose a new State-GMM-supervector extractor for solving the problem of text-dependent speaker recognition. The proposed scheme for supervector extraction makes it easy to implement a text-dependent JFA system for passphrase verification. We examine the conditions of both a global and a text-prompted passphrase. The experiments conducted on the Wells Fargo Bank speech database show that the proposed method makes it possible to create more accurate statistical models of speech signals and to achieve a 44% relative reduction of EER compared to the best state-of-the-art systems of text-dependent verification for a text-prompted passphrase.