Abstract
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.
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Kurogi, S., Mineishi, S., Sato, S. (2010). An Analysis of Speaker Recognition Using Bagging CAN2 and Pole Distribution of Speech Signals. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_45
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DOI: https://doi.org/10.1007/978-3-642-17537-4_45
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