Combining Multiple Acoustic Models in GMM Spaces for Robust Speech Recognition-Reference-Cited by-同舟云学术

Combining Multiple Acoustic Models in GMM Spaces for Robust Speech Recognition

Published:2016 Issue:3 Volume:E99.D Page:724-730
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KANG Byung Ok¹²,KWON Oh-Wook²

Affiliation:

1. SW Content Research Laboratory, ETRI

2. School of Electronics Engineering, Chungbuk National University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E99.D/3/E99.D_2015EDP7252/_pdf

Reference18 articles.

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2. [2] R.P. Lippmann, E.A. Martin, and D.B. Paul, “Multi-style training for robust isolated-word speech recognition,” Proc. ICASSP-1987, pp.705-708, Dallas, Texas, USA, May 1987.

3. [3] D. Povey, S.M. Chu, and B. Varadarajan, “Universal background model based speech recognition,” Proc. ICASSP-2008, Las Vegas USA, March 2008.

4. [4] D. Povey, L. Burget, M. Agarwal, P. Akyazi, K. Feng, A. Ghoshal, O. Glembek, N.K. Goel, M. Karafiat, A. Rastrow, R.C. Rose, P. Schwarz, and S. Thomas, “Subspace Gaussian mixture models for speech recognition,” Proc. ICASSP-2010, Dallas, Texas, USA, March 2010.

5. [5] U. Nallasamy, F. Metze, and T. Schultz, “Enhanced polyphone decision tree adaptation for accented speech recognition,” Proc. INTERSPEECH-2012, pp.1902-1905, 2012.

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