Estimation of Prior Probabilities in Speaker Recognition

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    4 Citations (Scopus)

    Abstract

    According to Bayesian decision theory, the maximum a posteriori (MAP) decision rule is used to minimize the speaker recognition error rate. The a posteriori probability is determined if the a priori probability and the likelihood function are known. However, there has been no method to determine the a priori probability, therefore the maximum likelihood (ML) decision rule is used instead. The paper proposes a method to estimate the a priori probability for speakers based on a training data set and speaker models. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the MAP rule showed lower error rates than using the ML rule.
    Original languageEnglish
    Title of host publicationProceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
    EditorsJ Kwok, LM Po
    Place of PublicationHong Kong
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages141-144
    Number of pages4
    ISBN (Print)0-7803-8688-4
    DOIs
    Publication statusPublished - 2004
    Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing - , Hong Kong
    Duration: 19 Oct 200421 Oct 2004

    Conference

    Conference2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
    CountryHong Kong
    Period19/10/0421/10/04

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    Cite this

    Tran, D. (2004). Estimation of Prior Probabilities in Speaker Recognition. In J. Kwok, & LM. Po (Eds.), Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing (pp. 141-144). Hong Kong: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIMP.2004.1434020