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

Jae C. Oh, Misty Blowers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationGECCO 2005 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages172-174
Number of pages3
ISBN (Print)1595930108, 9781595930101
DOIs
StatePublished - 2005
EventGECCO 2005 - Genetic and Evolutionary Computation Conference - Washington, D.C., United States
Duration: Jun 25 2005Jun 29 2005

Publication series

NameGECCO 2005 - Genetic and Evolutionary Computation Conference
Volume2005-January

Conference

ConferenceGECCO 2005 - Genetic and Evolutionary Computation Conference
Country/TerritoryUnited States
CityWashington, D.C.
Period6/25/056/29/05

Keywords

  • genetic classifier systems
  • open-set text-independent speaker identification

ASJC Scopus subject areas

  • Engineering(all)

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