Text-independent open-set speaker identification for military missions using genetic rule-based system

Authors:
Jae C. Oh;Misty Blowers
Affiliations:
Syracuse University, Syracuse, NY;AFRL/IFEC, Rome, NY
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

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Abstract

We present a genetic classifier system approach to the text-independent open-set speaker identification problem. Classifier systems are widely used in symbolic problem for dynamically changing open-ended learning. Signal processing problems require processing of real-valued parameters that classifier systems are not designed for. On the other hand, the approaches based on common cepstral encoding with clustering algorithms handle the closed-set speaker identification quite well. This research solves the open-set problem by hybridizing these two approaches.