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您的检索式：作者名="Chi Huisheng"
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| 1 | An enhanced relative spectral processing of speech显示文摘An enhanced relative spectral (FLRASTA) technique for speech and speaker recognition is proposed. The new method consists of classical RASTA filtering in logarithmic spectral domain following by another additive RASTA filtering in the same domain. In this manner, both the channel distortion and additive noise are removed effectively. In speaker identification and speech recognition experiments on T146 database, the E_RASTA performs equal or better than J_RASTA method in both tasks. The E_RASTA does not need the speech SNR estimation in order to determinate the optimal value of J in J_RASTA, and the information of how the speech degrades. The choice of ERASTA filter also indicates that the low temporal modulation components in speech can deteriorate the performance of both recognition tasks. Besides, the speaker recognition needs less temporal modulation frequency band than that of the speech recognition. | ZHEN Bin WU Xihong LIU Zhimin CHI Huisheng (Center for Information Science, Peking University Beijing 100871) | 2002 | Chinese Journal of Acoustics2002，21，1： | 2 |
| 2 | Methods of combining multiple classifiers with different features and their applica- tions to text-independent speaker identification显示文摘 | Chert Ke Wang Lan Chi Huisheng | 1997 | Interna- tional Journal of Pattern Recognition and Artificial Intelli- gence1997，11，3： | 1 |
| 3 | Capture Interspeaker Information With a Neural Network for Speaker Identification显示文摘 | Ke Chen Huisheng Chi | 2002 | IEEE TRANSACTIONS ON NEURAL NETWORKS2002，13，2： | 1 |
| 4 | Product HMM-based training method for acoustic model with multiple-size units显示文摘Multiple-size units-based acoustic modeling has been proposed for large vocabulary speech recognition system to improve the recognition accuracy with limited training data.By introducing a limited number of long-size units into unit set,this modeling scheme can make better acoustic model precision than complete short-size unit modeling without losing model trainability.However,such a multiple-size unit acoustic modeling paradigm does not always bring reliable improvement on recognition performance,since when a large number of long-size units are added in,the amount of training data for short-size units will decrease and result in insufficiently trained models.In this paper,a modified Baum-Welch training method is proposed,which uses product hidden Markov models(PHMMs)to couple units with different sizes and enables them to share same portions of training data.The validity of proposed method is proved by experiment results. | Hao WU Xihong WU Huisheng CHI | 2010 | Frontiers of Electrical and Electronic Engineering in China2010，5，1： | 0 |
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