標題: A kernel-based discrimination framework for solving hypothesis testing problems with application to speaker verification
作者: Chao, Yi-Hsiang
Tsai, Wei-Ho
Wang, Hsin-Min
Chang, Ruei-Chuan
資訊工程學系
Department of Computer Science
公開日期: 2006
摘要: Real-word applications often involve a binary hypothesis testing problem with one of the two hypotheses ill-defined and hard to be characterized precisely by a single measure. In this paper, we develop a framework that integrates multiple hypothesis testing measures into a unified decision basis, and apply kernel-based classification techniques, namely, Kernel Fisher Discriminant (KFD) and Support Vector Machine (SVM), to optimize the integration. Experiments conducted on speaker verification demonstrate the superiority of our approaches over the predominant approaches.
URI: http://hdl.handle.net/11536/135182
ISBN: 0-7695-2521-0
ISSN: 1051-4651
期刊: 18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS
起始頁: 229
結束頁: +
顯示於類別:會議論文