Modern speaker verification applications require high accuracy at low complexity. We propose the use of a polynomial-based classifier to achieve this objective. We demonstrate a new combination of techniques which makes polynomial classification accurate and powerful for speaker verification. We show that discriminative training of polynomial classifiers can be performed on large data sets. A prior probability compensation method is detailed which increases accuracy and normalizes the output score range. Results are given for the application of the new methods to YOHO.
|Journal||Data powered by Typeset1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)|
|Publisher||Data powered by TypesetIEEE|