DocumentCode
2020902
Title
Notice of Violation of IEEE Publication Principles
An automatic data-driven technique for selecting background dataset in GMM-SVM speaker verification system
Author
Jinchao Yang ; Haipeng Wang ; Jianping Zhang ; Yonghong Yan
Author_Institution
Think IT Speech Lab., Chinese Acad. of Sci., Beijing, China
fYear
2010
fDate
23-25 Nov. 2010
Firstpage
85
Lastpage
89
Abstract
Notice of Violation of IEEE Publication Principles
"An Automatic Data-driven Technique for Selecting Background Dataset in GMM-SVM Speaker Verification System"
by Jinchao Yang, Haipeng Wang, Jianping Zhang, Yonghong Yan International Conference on Audio Language and Image Processing (ICALIP), 2010, pp. 85-89
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Improved SVM Speaker Verification Through Data-Driven Background Dataset Selection"
by Mitchell McLaren, Brendan Baker, Robbie Vogt, Sridha Sridharan
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2009, pp. 4041-4044
In this paper, we propose an automatic data-driven technique for selecting proper background dataset. By the technique, impostor confidence(IC) is proposed as a metric and more discriminative background dataset is automatically chose by impostor confidence(IC) to train more discriminative model. Experiment results on NIST 2008 SRE corpus in GMM-SVM speaker verification system show that the proposed approach obtains better performance. Relative decline in mincost of 8.9% in female and 4.6% in male is obtained. with female and male combined, 5.4% relative decline in mincost is obtained over Heuristically selected background dataset.
"An Automatic Data-driven Technique for Selecting Background Dataset in GMM-SVM Speaker Verification System"
by Jinchao Yang, Haipeng Wang, Jianping Zhang, Yonghong Yan International Conference on Audio Language and Image Processing (ICALIP), 2010, pp. 85-89
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Improved SVM Speaker Verification Through Data-Driven Background Dataset Selection"
by Mitchell McLaren, Brendan Baker, Robbie Vogt, Sridha Sridharan
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2009, pp. 4041-4044
In this paper, we propose an automatic data-driven technique for selecting proper background dataset. By the technique, impostor confidence(IC) is proposed as a metric and more discriminative background dataset is automatically chose by impostor confidence(IC) to train more discriminative model. Experiment results on NIST 2008 SRE corpus in GMM-SVM speaker verification system show that the proposed approach obtains better performance. Relative decline in mincost of 8.9% in female and 4.6% in male is obtained. with female and male combined, 5.4% relative decline in mincost is obtained over Heuristically selected background dataset.
Keywords
Gaussian distribution; speaker recognition; support vector machines; GMM-SVM speaker verification; Gaussian mixture model; NIST 2008 SRE corpus; automatic data driven technique; discriminative background dataset; impostor confidence; support vector machine; Data models; Integrated circuit modeling; Speech; Support vector machines; System performance; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Audio Language and Image Processing (ICALIP), 2010 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-5856-1
Type
conf
DOI
10.1109/ICALIP.2010.5685009
Filename
5685009
Link To Document