Discriminative training of GMM-HMM acoustic model by RPCL learning

Author:

Pang Zaihu,Tu Shikui,Su Dan,Wu Xihong,Xu Lei

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Reference19 articles.

1. Brown P. The acoustic-modeling problem in automatic speech recognition. Dissertation for the Doctoral Degree. Pittsburgh: Carnegie Mellon University, 1987

2. Gales M, Young S. The application of hidden Markov models in speech recognition. Foundations and Trends in Signal Processing, 2008, 1(3): 195–304

3. Bahl L, Brown P, De Souza P, Mercer R. Maximum mutual information estimation of hidden Markov model parameters for speech recognition. In: Proceedings of 1986 IEEE International Conference on Acoustics, Speech, and Signal Processing. 1986, 11: 49–52

4. Juang B H, Chou W, Lee C H. Minimum classification error rate methods for speech recognition. IEEE Transactions on Speech and Audio Processing, 1997, 5(3): 257–265

5. Povey D, Woodland P C. Minimum phone error and Ismothing for improved discriminative training. In: Proceedings of 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. 2002, 1: 105–108

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