On a Code-Excited Nonlinear Predictive Speech Coding(CENLP) by Means of Recurrent Neural Networks(Special Section on Digital Signal Processing)
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概要
- 論文の詳細を見る
To improve speech coding quality, in particular, the long-term dependency prediction characteristics, we propose a new nonlinear predictor, i.e., a fully connected recurrent neural network(FCRNN) where the hidden units have feedbacks not only from themselves but also from the output unit. The comparison of the capabilities of the FRCNN with conventional predictors shows that the former has less prediction error than the latter. We apply this FRCNN instead of the previously proposed recurrent neural networks in the code-excited predictive speech coding system(i.e., CELP)and shows that our system (FCRNN)requires less bit rate/frame and improves the performance for speech coding.
- 社団法人電子情報通信学会の論文
- 1998-08-25
著者
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Wei Gang
Niigata University Venture Business Laboratory
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Nishi Tetsuo
Waseda Univ. Tokyo
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Nishi T
Kyushu Univ. Fukuoka Jpn
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Nishi T
Dept.of Computer Science And Communication Eng. Faculty Of Information Science And Electrical Eng. K
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Nishi Tetsuo
Faculty Of Science And Engineering Waseda Univ.
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Nishi Tetsuo
Department Of Computer Science And Communication Engineering Kyushu Univ.
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Nishi T
Faculty Of Science And Engineering Waseda Univ.
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MA Ni
Department of Computer Science and Communication Engineering, Kyushu University
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WEI Gang
Department of Electronics and Communication Engineering, SCUT
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Ma Ni
Dept.of Computer Science And Communication Eng. Grad.school Of Inform. Sci.and Electrical Eng. Kyush
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