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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Properties of Text-Prompted Multistep Speaker Verification Using Gibbs-Distribution-Based Extended Bayesian Inference for Rejecting Unregistered Speakers
verfasst von : Shuichi Kurogi, Takuya Ueki, Satoshi Takeguchi, Yuta Mizobe
Erschienen in: Neural Information Processing
Verlag: Springer International Publishing
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This paper presents a method of text-prompted multistep speaker verification for reducing verification errors and rejecting unregistered speakers. The method has been developed for our speech processing system which utilizes competitive associative nets (CAN2s) for learning piecewise linear approximation of nonlinear speech signal to extract feature vectors of pole distribution from piecewise linear coefficients reflecting nonlinear and time-varying vocal tract of the speaker. This paper focuses on rejecting unregistered speakers by means of multistep verification using Gibbs-distribution-based extended Bayesian inference (GEBI) in text-prompted speaker verification. The properties of GEBI and the comparison to BI (Bayesian inference) for rejecting unregistered speakers are shown and analyzed by means of experiments using real speech signals.