Abstract
This paper presents a method of multistep speaker recognition using naive Bayesian inference and competitive associative nets (CAN2s). We have been examining a method of speaker recognition using feature vectors of pole distribution extracted by the bagging CAN2, where the CAN2 is a neural net for learning piecewise linear approximation of nonlinear function, and bagging CAN2 is the bagging (bootstrap aggregating) version. In order to reduce the recognition error, we formulate a multistep recognition using naive Bayesian inference. After introducing several modifications for reasonable recognition, we show the effectiveness of the present method by means of sereral experiments using real speech signals.
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Kurogi, S., Mineishi, S., Tsukazaki, T., Nishida, T. (2011). Naive Bayesian Multistep Speaker Recognition Using Competitive Associative Nets. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_9
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DOI: https://doi.org/10.1007/978-3-642-24955-6_9
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