Notice of Violation of IEEE Publication Principles: An automatic data-driven technique for selecting background dataset in GMM-SVM speaker verification system

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.