Abstract
This paper presents a method of multistep speaker identification using Gibbs-distribution-based extended Bayesian inference (GEBI) for rejecting unregistered speaker. The method is developed for our speaker recognition 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. In this paper, we focus on the problem of Bayesian inference (BI) in multistep identification for rejecting unregistered speaker and introduce GEBI to solve the problem. The effectiveness of the present method is shown by means of experiments using real speech signals.
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Mizobe, Y., Kurogi, S., Tsukazaki, T., Nishida, T. (2012). Multistep Speaker Identification Using Gibbs-Distribution-Based Extended Bayesian Inference for Rejecting Unregistered Speaker. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_30
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DOI: https://doi.org/10.1007/978-3-642-34500-5_30
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