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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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On-Line Linear Combination of Classifiers Based on Incremental Information in Speaker Verification

  • Huenupan, Fernando (Speech Processing and Transmission Laboratory, Department of Electrical Engineering, Universidad de Chile) ;
  • Yoma, Nestor Becerra (Speech Processing and Transmission Laboratory, Department of Electrical Engineering, Universidad de Chile) ;
  • Garreton, Claudio (Speech Processing and Transmission Laboratory, Department of Electrical Engineering, Universidad de Chile) ;
  • Molina, Carlos (Speech Processing and Transmission Laboratory, Department of Electrical Engineering, Universidad de Chile)
  • Received : 2009.05.27
  • Accepted : 2010.01.12
  • Published : 2010.06.30
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Abstract

A novel multiclassifier system (MCS) strategy is proposed and applied to a text-dependent speaker verification task. The presented scheme optimizes the linear combination of classifiers on an on-line basis. In contrast to ordinary MCS approaches, neither a priori distributions nor pre-tuned parameters are required. The idea is to improve the most accurate classifier by making use of the incremental information provided by the second classifier. The on-line multiclassifier optimization approach is applicable to any pattern recognition problem. The proposed method needs neither a priori distributions nor pre-estimated weights, and does not make use of any consideration about training/testing matching conditions. Results with Yoho database show that the presented approach can lead to reductions in equal error rate as high as 28%, when compared with the most accurate classifier, and 11% against a standard method for the optimization of linear combination of classifiers.

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