Automatic Parameter Switching of Noise Reduction for Speech Recognition
Author:
Affiliation:
1. NTT Media Intelligence Laboratories, NTT Corporation
2. Department of Mechanical Engineering, University of Auckland
Publisher
Research Institute of Signal Processing, Japan
Link
https://www.jstage.jst.go.jp/article/jsp/21/2/21_63/_pdf
Reference26 articles.
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3. [3] T. Hori, C. Hori, Y. Minami and A. Nakamura: Efficient WFST-based one-pass decoding with on-the-fly hypothesis rescoring in extremely large vocabulary continuous speech recognition, IEEE Trans. on Audio, Speech, and Language Processing, Vol. 15, No. 4, pp. 1352-1365, 2007.
4. [4] G. Hinton, S. Osindero and Y. Teh: A fast learning algorithm for deep belief nets, Neural Comput., Vol. 18, No. 7, pp. 1527-1554, 2006.
5. [5] M. L. Seltzer, D. Yu and Y. Wang: An investigation of deep neural networks for noise robust speech recognition, Proc. International Conference on Acoustics, Speech, and Signal Processing, pp. 7398-7402, 2013.
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