Improved SVM speaker verification through data-driven background dataset selection
McLaren, Mitchell L., Baker, Brendan J., Vogt, Robert J., & Sridhara, Sridha (2009) Improved SVM speaker verification through data-driven background dataset selection. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 19-24 April 2009, Taipei, Taiwan.
The problem of background dataset selection in SVM-based speaker verification is addressed through the proposal of a new data-driven selection technique. Based on support vector selection, the proposed approach introduces a method to individually assess the suitability of each candidate impostor example for use in the background dataset. The technique can then produce a refined background dataset by selecting only the most informative impostor examples. Improvements of 13% in min. DCF and 10% in EER were found on the SRE 2006 development corpus when using the proposed method over the best heuristically chosen set. The technique was also shown to generalise to the unseen NIST 2008 SRE corpus.
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|Item Type:||Conference Paper|
|Keywords:||Speaker recognition, Data selection, Support Vector Machines|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2009 IEEE|
|Deposited On:||08 May 2009 08:14|
|Last Modified:||29 Feb 2012 23:54|
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