Naive bayesian multistep speaker recognition using competitive associative nets

Authors:
Shuichi Kurogi;Shota Mineishi;Tomohiro Tsukazaki;Takeshi Nishida
Affiliations:
Kyusyu Institute of technology, Kitakyushu, Fukuoka, Japan;Kyusyu Institute of technology, Kitakyushu, Fukuoka, Japan;Kyusyu Institute of technology, Kitakyushu, Fukuoka, Japan;Kyusyu Institute of technology, Kitakyushu, Fukuoka, Japan
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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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.