Open-set speaker identification with classifier systems

Jae C. Oh, Misty Blowers

Research output: Chapter in Book/Entry/PoemConference contribution


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.

Original languageEnglish (US)
Title of host publicationModeling and Simulation for Military Applications
StatePublished - 2006
EventModeling and Simulation for Military Applications - Kissimmee, FL, United States
Duration: Apr 18 2006Apr 21 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherModeling and Simulation for Military Applications
Country/TerritoryUnited States
CityKissimmee, FL


  • Genetic Classifier Systems
  • Learning and Recognition
  • Speaker Identification Problem

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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