Speaker verification using verbal information verification for automatic enrolment

A conventional speaker verification (SV) system needs an enrollment session to collect the training data. In [I], we introduced a speaker authentication method called verbal information ven$cation (VIV) which verifies a speaker by verbal contents instead of speech characteristics. Such a system does not need an enrollment session. In this paper, VIV is combined with SV. We propose a system which uses VIV to collect training data during the first few accesses automatically, which are often from different acoustic environments. Then, a speaker dependent model is trained and speaker authentication can be performed by SV. This approach not only avoid formal enrollment session which brings convenience to the user, but mitigates the mismatch problem causing by different acoustic environments between training and test sessions. Our experiments show that the proposed system improved the SV performance over 40% compared to the conventional SV system.