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Proceedings Paper

Open-set speaker identification with classifier systems
Author(s): Jae C. Oh; Misty Blowers
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Paper Abstract

Signal processing problems including the speaker identification problem require processing of real-valued feature vectors. Traditional cepstral encoding combined with clustering algorithms handle the closed-set speaker identification problem quite well but when it comes to the open-set problem, clustering methods show lack of performance. Furthermore, many clustering algorithms lack adaptability and the ability to learn on-the-fly. Genetic classifier systems are adaptive and they have the ability for open-ended learning. We introduce a genetic classifier system approach to the speaker identification problem and several classifier knowledge representation methods for open-set speaker identification. Experimental results show that the new system works quite well for the open-set speaker identification problem.

Paper Details

Date Published: 22 May 2006
PDF: 13 pages
Proc. SPIE 6228, Modeling and Simulation for Military Applications, 62280Y (22 May 2006); doi: 10.1117/12.668791
Show Author Affiliations
Jae C. Oh, Syracuse Univ. (United States)
Misty Blowers, Air Force Research Lab., IFEC (United States)

Published in SPIE Proceedings Vol. 6228:
Modeling and Simulation for Military Applications
Kevin Schum; Alex F. Sisti, Editor(s)

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