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New Developments in Voice Biometrics for User Authentication

Voice biometrics for user authentication is a task in which the object is to perform convenient, robust and secure authentication of speakers. In this work we investigate the use of state-of-the-art text-independent and text-dependent speaker verification technology for user authentication. We evaluate four different authentication conditions: speaker specific digit stings, global digit strings, prompted digit strings, and text-independent. Harnessing the characteristics of the different types of conditions can provide benefits such as authentication transparent to the user (convenience), spoofing robustness (security) and improved accuracy (reliability). The systems were evaluated on a corpus collected by Wells Fargo Bank which consists of 750 speakers. We show how to adapt techniques such as joint factor analysis (JFA), Gaussian mixture models with nuisance attribute projection (GMM-NAP) and hidden Markov models with NAP (HMM-NAP) to obtain improved results for new authentication scenarios and environments.