DocumentCode
1958935
Title
Notice of Retraction
Optimal factor analysis with GMM mixture pre-selection
Author
Ling Huang ; Jinhong Zhong
Author_Institution
Key State Lab. of Laser Pulse Power Technol., PLA, Hefei, China
Volume
9
fYear
2010
fDate
9-11 July 2010
Firstpage
696
Lastpage
699
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The problem of session variability in text-independent speaker verification has been tackled actively for a few years. Factor analysis has successfully been applied for this problem. However, efficient implementation of factor analysis is also a challenge. In this paper we propose a novel GMM mixture pre-selection based factor analysis model for speaker verification and show how it successfully implemented using some fast approximations which result in minor degradation in accuracy. We tested the novel algorithm on the NIST SRE 2006 evaluation set. With similarly accuracy as factor analysis, the efficiency is improved about 10 times.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The problem of session variability in text-independent speaker verification has been tackled actively for a few years. Factor analysis has successfully been applied for this problem. However, efficient implementation of factor analysis is also a challenge. In this paper we propose a novel GMM mixture pre-selection based factor analysis model for speaker verification and show how it successfully implemented using some fast approximations which result in minor degradation in accuracy. We tested the novel algorithm on the NIST SRE 2006 evaluation set. With similarly accuracy as factor analysis, the efficiency is improved about 10 times.
Keywords
Gaussian distribution; approximation theory; speaker recognition; GMM mixture pre-selection; Gaussian mixture model; NIST SRE 2006 evaluation set; approximations; optimal factor analysis; text-independent speaker verification; Analytical models; Computational modeling; Factor analysis; Gaussian Mixture Model (GMM); Speaker verification;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5565083
Filename
5565083
Link To Document