Minimum Classification Error rate method using genetic algorithms


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2 Citations (Scopus)


Hidden Markov Models (HMM) is one the most common statistical matching methods used for speech recognition, especially for continuous speech utterances. One major problem in HMM is that the training process that aims to generate a set of HMM models (recognizer) for matching the speech source usually based on a set of limited training data. The Minimum Classification Error (MCE) training approach proposed by Juang [9] is regarded as a discriminative method that is proven to be superior to other traditional probability distribution estimation approaches, such as the Maximum likelihood (ML) approach. The main drawback in the MCE is to formulate the error rate estimate junction as a smooth loss junction for applying gradient search technique that subsequently leads to a local optimal solution. In this paper, a genetic algorithm based approach (GA-MCE) for the MCE is proposed to solve these problems. In our experiments, the results demonstrated that the GA-MCE is superior to the original MCE method. It can be also significantly increased the performance of voice input systems.
Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Publication statusPublished - 2000
Externally publishedYes


  • Error analysis
  • Genetic algorithms
  • Hidden Markov models
  • Inference algorithms
  • Maximum likelihood estimation
  • Parameter estimation
  • Speech processing
  • Speech recognition
  • Training data
  • Vocabulary


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