Competitive learning algorithms for vector quantization
Neural Networks
Machine Learning
Survey of the state of the art in human language technology
Reproduction and Recognition of Vowel Signals Using Single and Bagging Competitive Associative Nets
Neural Information Processing
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
Speaker Recognition Using Pole Distribution of Speech Signals Obtained by Bagging CAN2
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Naive bayesian multistep speaker recognition using competitive associative nets
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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A method of speaker recognition which uses feature vectors of pole distribution derived from piecewise linear predictive coefficients obtained by bagging CAN2 (competitive associative net 2) is presented and analyzed. The CAN2 is a neural net for learning efficient piecewise linear approximation of nonlinear function, and the bagging CAN2 (bootstrap aggregating version of CAN2) is used to obtain statistically stable multiple linear predictive coefficients. From the coefficients, the present method obtains a number of poles which are supposed to reflect the shape of the speaker's vocal tract. Then, the pole distribution is used as a feature vector for speaker recognition. The effectiveness is analyzed and validated using real speech data.