ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

An environment model-based robust speech recognition

Lei Zhang, Jiqing Han, Chengguo Lv, Chengfa Wang

In this paper, a new approach named environmental discrimination learning (EDL) is proposed to remove the effects of the environment noises including the additive noise and the channel distortions. This method optimizes the environment parameters by the minimum classification error (MCE) criterion which trains the parameters of a given class dependently on the whole classes. The EDL approach utilizes more about the information between different classes to optimize the environment parameters, therefore, it can minimize the error rate. And a generalized probabilistic descent (GPD) algorithm is adopted for discriminative training the environment parameters. A speaker independent isolated word recognition system based on whole word-HMM model is used to evaluate the proposed approach. Experimental results show that the proposed method achieves significant improvement of recognition performance.


Cite as: Zhang, L., Han, J., Lv, C., Wang, C. (2000) An environment model-based robust speech recognition. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 590-593

@inproceedings{zhang00g_icslp,
  author={Lei Zhang and Jiqing Han and Chengguo Lv and Chengfa Wang},
  title={{An environment model-based robust speech recognition}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 3, 590-593}
}