DocumentCode :
1691934
Title :
Anti-model KL-SVM-NAP system for NIST SRE 2012 evaluation
Author :
Hanwu Sun ; Kong Aik Lee ; Bin Ma
Author_Institution :
Inst. for Infocomm Res. (I2R), A*STAR, Singapore, Singapore
fYear :
2013
Firstpage :
7688
Lastpage :
7692
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.
Keywords :
learning (artificial intelligence); speaker recognition; support vector machines; IIR SRE12 submission; KL-SVM-NAP based speaker recognition system; NIST SRE 2012 evaluation; anti-model based speaker recognition system; Compounds; NIST; Noise measurement; Speaker recognition; Speech; Support vector machines; Training; Nuisance Attribute Projection; anti-model; speaker recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6639159
Filename :
6639159
Link To Document :
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