Long short-term memory recurrent-neural-network-based bandwidth extension for automatic speech recognition-Reference-Cited by-同舟云学术

Long short-term memory recurrent-neural-network-based bandwidth extension for automatic speech recognition

Published:2016 Issue:6 Volume:37 Page:319-321
ISSN:1346-3969
Container-title:Acoustical Science and Technology
language:en
Short-container-title:Acoust. Sci. & Tech.

Author:

Tachioka Yuuki¹,Ishii Jun¹

Affiliation:

1. Information Technology R&D Center, Mitsubishi Electric Corporation

Publisher

Acoustical Society of Japan

Subject

Acoustics and Ultrasonics

Link

https://www.jstage.jst.go.jp/article/ast/37/6/37_E1622/_pdf

Reference8 articles.

1. 1) Y. Wang, S. Zhao, Y. Yu and J. Kuang, ``Speech bandwidth extension based on GMM and clustering method,'' Proc. 5th Int. Conf. Communication Systems and Network Technologies (CSNT), pp. 437-441 (2015).

2. 2) S. Hochreiter and J. Schmidhuber, ``Long short-term memory,'' Neural Comput., 9, 1735-1780 (1997).

3. 3) T. Toda, A. W. Black and K. Tokuda, ``Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory,'' IEEE Trans. Audio Speech Lang. Process., 15, 2222-2235 (2007).

4. 4) F. Weninger, J. Geiger, M. Wöllmer, B. Schuller and G. Rigoll, ``The Munich feature enhancement approach to the 2nd CHiME challenge using BLSTM recurrent neural networks,'' Proc. 2nd CHiME Workshop Machine Listening in Multisource Environments, pp. 86-90 (2013).

5. 5) D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, M. Petr, Y. Qian, P. Schwarz, J. Silovský, G. Stemmer and K. Veselý, ``The Kaldi speech recognition toolkit,'' Proc. ASRU, pp. 1-4 (2011).

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