An open-set speaker identification system using genetic learning classifier system

Won Kyung Park, Misty K. Blowers, Jae C. Oh, Matt B. Wolf

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

2 Scopus citations

Abstract

This paper presents the design and implementation of an adaptive open-set speaker identification system with genetic learning classifier systems. One of the challenging problems in using learning classifier systems for numerical problems is the knowledge representation. The voice samples are a series of real numbers that must be encoded in a classifier format. We investigate several different methods for representing voice samples for classifier systems and study the efficacy of the methods. We also identify several challenges for learning classifier systems in the speaker identification problem and introduce new methods to improve the learning and classification abilities of the systems. Experimental results show that our system successfully learns 200 voice features at the accuracies of 60% to 80%, which is considered a strong result in the speaker identification community. This research presents the feasibility of using learning classifier systems for the speaker identification problem.

Original languageEnglish (US)
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery (ACM)
Pages1597-1598
Number of pages2
ISBN (Print)1595931864, 9781595931863
DOIs
StatePublished - 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: Jul 8 2006Jul 12 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume2

Other

Other8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA
Period7/8/067/12/06

Keywords

  • Classifier systems
  • Genetic algorithms
  • Language and speech
  • Linguistics
  • Machine learning

ASJC Scopus subject areas

  • Engineering(all)

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