Paper Info
Title
Effective Background Data Selection In Svm Speaker Recognition For Unseen Test Environment: More Is Not Always Better
Abstract
This study focuses on determining a procedure to select effective negative examples for development of improved Support Vector Machine (SVM) based speaker recognition. Selection of a background dataset, comprising of a group of negative examples, is critical in development of an effective decision surface between the primary speaker and outside speaker rejection space. Previous studies generally fix the number of examples based on development data for system performance evaluation, while for real applications this does not guarantee sustained performance for unseen data. In the proposed method, the error is estimated on the support vector to select the background dataset, thereby by customizing the background dataset for each enrollment speaker instead of training models with a fixed background data. The proposed method finds the equivalent or improved EER and DCF compared with the previous SVM-based studies, and provides consistent performance for unseen data. The method improves the 6% relative improvement on EER and DCF for NIST SRE 2010.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5947555
2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
Keywords
Field
DocType
Speaker recognition, background dataset selection, support vector machine, data evaluation
Data selection,Pattern recognition,Computer science,Support vector machine,Measurement uncertainty,Speech recognition,NIST,Speaker recognition,Artificial intelligence,Covariance matrix,Decision boundary,Machine learning
Conference
ISSN
Citations 
PageRank 
1520-6149
3
0.40
References 
Authors
6
4
Name
Order
Citations
PageRank
Jun-Won Suh1102.37
Yun Lei230621.55
Wooil Kim312016.95
John H. L. Hansen43215365.75