QUT ePrints

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

McLaren, Mitchell L., Vogt, Robert J., Baker, Brendan J., & Sridharan, Sridha (2009) Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification. In Massimo, Tistarelli & Nixon, Mark S. (Eds.) Advances in Biometrics : Third International Conferences, ICB 2009, Alghero, Italy, June 2-5, 2009, Proceedings. Springer , Berlin Heidelberg, pp. 474-483.

[img] Accepted Version (PDF 798kB)
Administrators only | Request a copy from author

    View at publisher


    A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

    Impact and interest:

    2 citations in Scopus
    Search Google Scholar™
    1 citations in Web of Science®

    Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

    These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

    Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

    ID Code: 29485
    Item Type: Book Chapter
    Additional URLs:
    Keywords: Speaker recognition, Data selection, Support vector machines, Score Normalisation
    DOI: 10.1007/978-3-642-01793-3_49
    ISBN: 3642017924
    ISSN: 0302-9743
    Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
    Past > Institutes > Information Security Institute
    Past > Schools > School of Engineering Systems
    Copyright Owner: Copyright 2009 Springer
    Copyright Statement: The original publication is available at SpringerLink http://www.springerlink.com
    Deposited On: 06 Jan 2010 08:31
    Last Modified: 18 Jul 2014 10:07

    Export: EndNote | Dublin Core | BibTeX

    Repository Staff Only: item control page