Speaker verification using sparse representation classification

J.M.K. Kua, E. Ambikairajah, J. Epps, Roberto Togneri

    Research output: Chapter in Book/Conference paperConference paper

    57 Citations (Scopus)


    Sparse representations of signals have received a great deal of attention in recent years, and the sparse representation classifier has very lately appeared in a speaker recognition system. This approach represents the (sparse) GMM mean supervector of an unknown speaker as a linear combination of an over-complete dictionary of GMM supervectors of many speaker models, and l(1)-norm minimization results in a non-zero coefficient corresponding to the unknown speaker class index. Here this approach is tested on large databases, introducing channel-/session-variability compensation, and fused with a GMM-SVM system. Evaluations on the NIST 2001 SRE and NIST 2006 SRE database show that when the outputs of the MFCC UBM-GMM based classifier (for NIST 2001 SRE) or MFCC GMM-SVM based classifier (for NIST 2006 SRE) are fused with the MFCC GMM-Sparse Representation Classifier (GMM-SRC) based classifier, an absolute gain of 1.27% and 0.25% in EER can be achieved respectively.
    Original languageEnglish
    Title of host publication2011 IEEE International Conference On Acoustics, Speech, And Signal Processing
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    ISBN (Print)9781457705397
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011 - Prague, Czech Republic
    Duration: 22 May 201127 May 2011


    Conference2011 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011
    Abbreviated titleICASSP 2011
    CountryCzech Republic

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