Support vector domain description for speaker recognition
Xin Dong, Zhaohui Wu, Zhang Wanfeng · 2002
A novel approach to speaker recognition is presented. The method, called Support Vector Data Description (SVDD), was originally suggested by Vapnik, interpreted as a novelty detector by D. Tax and R. Duin (1999). In this paper, we use this data domain description as a classifier. It contains support vectors describing the sphere separating the samples. With a minimal radius R, this classifier achieves good performance in finding abnormal samples within the open set test. We use it in the speaker identification application. The results on YOHO database are presented.