Speaker identification based on adaptive discriminative vector quantisation

    Authors

    G. Zhou;W. B. Mikhael

    Comments

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    Abbreviated Journal Title

    IEE Proc.-Vis. Image Signal Process.

    Keywords

    HIDDEN MARKOV-MODELS; RECOGNITION; Engineering, Electrical & Electronic

    Abstract

    A novel adaptive discriminative vector quantisation technique for speaker identification (ADVQSI) is introduced. In the training mode of ADVQSI, for each speaker, the speech feature vector space is divided into a number of subspaces. The feature space segmentation is based on the difference between the probability distribution of the speech feature vectors from each speaker and that from all speakers in the speaker identification (SI) group. Then, an optimal discriminative weight, which represents the subspace's role in SI, is calculated for each subspace of each speaker by employing adaptive techniques. The largest template differences between speakers in the SI group are achieved by using optimal discriminative weights. In the testing mode of ADVQSI, discriminative weighted average vector quantisation (VQ) distortions are used for SI decisions. The performance of ADVQSI is analysed and tested experimentally. The experimental results confirm the performance improvement employing the proposed technique in comparison with existing VQ techniques for SI and recently reported discriminative VQ techniques for SI (DVQSI).

    Journal Title

    Iee Proceedings-Vision Image and Signal Processing

    Volume

    153

    Issue/Number

    6

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    754

    Last Page

    760

    WOS Identifier

    WOS:000243043000006

    ISSN

    1350-245X

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