Estimation of Prior Probabilities in Speaker Recognition

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

4 Citations (Scopus)
19 Downloads (Pure)

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
Country/TerritoryHong Kong
Period19/10/0421/10/04

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