An iterative technique for training speaker verification systems

As biometrics progresses from the lab into practical embedded applications, the need for systems that are computationally simple, memory efficient, and accurate becomes a priority. Polynomial classification systems have high potential to fit these requirements. Previous work has shown that polynomial techniques applied to speaker verification lead to accurate systems with simple multiply-add structures well-fitted to DSP architectures. One of the challenges of the polynomial method is to find memory efficient techniques for training. We show that through a simple matrix index mapping technique combined with iterative training, memory requirements can be reduced drastically in training. We apply the new method to the YOHO database to show the equivalence of the method to prior approaches.