Open-set speaker identification with classifier systems

Jae C. Oh; Misty Blowers

doi:10.1117/12.668791

22 May 2006 Open-set speaker identification with classifier systems

Jae C. Oh, Misty Blowers

Proceedings Volume 6228, Modeling and Simulation for Military Applications; 62280Y (2006) https://doi.org/10.1117/12.668791
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Jae C. Oh and Misty Blowers "Open-set speaker identification with classifier systems", Proc. SPIE 6228, Modeling and Simulation for Military Applications, 62280Y (22 May 2006); https://doi.org/10.1117/12.668791

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