Paper Info
Title
Anti-model KL-SVM-NAP system for NIST SRE 2012 evaluation
Abstract
This paper presents an anti-model based speaker recognition system for NIST SRE 2012 evaluation, which is one of subsystems in IIR SRE12 submission. We apply the anti-model approach for the SRE12 evaluation. The KL-SVM-NAP based speaker recognition system is adopted to evaluate the performance. We present detailed comparison study of the classical KL-SVM-NAP based speaker recognition system and anti-model based KL-SVM-NAP system for NIST 2012 speaker recognition evaluation. The results are reported on in-house pre-SRE12 development set and NIST SRE12 core task. The clear advantages of the anti-model approach over that the traditional KL-SVM-NAP approach are presented and discussed.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639159
ICASSP
Keywords
Field
DocType
nist sre 2012 evaluation,nuisance attribute projection,learning (artificial intelligence),speaker recognition,iir sre12 submission,anti-model,anti-model based speaker recognition system,kl-svm-nap based speaker recognition system,support vector machines,learning artificial intelligence,noise measurement,nist,speech
Pattern recognition,Computer science,Support vector machine,Infinite impulse response,Speech recognition,Speaker recognition system,NIST,Speaker recognition,Artificial intelligence,Speaker diarisation
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.37
References 
Authors
8
3
Name
Order
Citations
PageRank
Hanwu Sun19814.15
Kong-Aik Lee270960.64
Bin Ma360047.26