Competitive learning algorithms for vector quantization
Neural Networks
Survey of the state of the art in human language technology
Speaker recognition using syllable-based constraints for cepstral frame selection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An analysis of speaker recognition using bagging CAN2 and pole distribution of speech signals
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Fundamentals of Speaker Recognition
Fundamentals of Speaker Recognition
Naive bayesian multistep speaker recognition using competitive associative nets
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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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.