Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

@article{McDermott2007DiscriminativeTF,
  title={Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error},
  author={Erik McDermott and Timothy J. Hazen and Jonathan Le Roux and Atsushi Nakamura and Shigeru Katagiri},
  journal={IEEE Transactions on Audio, Speech, and Language Processing},
  year={2007},
  volume={15},
  pages={203-223}
}
The minimum classification error (MCE) framework for discriminative training is a simple and general formalism for directly optimizing recognition accuracy in pattern recognition problems. The framework applies directly to the optimization of hidden Markov models (HMMs) used for speech recognition problems. However, few if any studies have reported results for the application of MCE training to large-vocabulary, continuous-speech recognition tasks. This article reports significant gains in… CONTINUE READING
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Segmental GPD training of HMM based speech recognizer

  • W. Chou, B.-H. Juang, C.-H. Lee
  • Proc. IEEE ICASSP, Mar. 1992, vol. 1, pp. 473–476…
  • 1992
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Guest editorial

  • L. Deng, K. Wang, W. Chou
  • IEEE Signal Process. Mag., vol. 22, no. 5, pp. 12…
  • 2005

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