A Kernel-based Discrimination Framework for Solving Hypothesis Testing Problems with Application to Speaker Verification

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, an...

Full description

Saved in:
Bibliographic Details
Main Authors: Yi-Hsiang Chao, Wei-Ho Tsai, Hsin-Min Wang, Ruei-Chuan Chang
Format: Conference Proceeding
Language:English
Subjects:
Application software
Chaos
Information science
Kernel
Loss measurement
Solid modeling
Speech
Support vector machine classification
Support vector machines
Testing
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2006.89