Discriminative Learning of Filterbank Layer within Deep Neural Network Based Speech Recognition for Speaker Adaptation
Author:
Affiliation:
1. Department of Computer Science and Engineering, Toyohashi University of Technology
2. Department of Computer Science, Chubu 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/E102.D/2/E102.D_2018EDP7252/_pdf
Reference50 articles.
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2. [2] T.N. Sainath, R.J. Weiss, A. Senior, K.W. Wilson, and O. Vinyals, “Learning the speech front-end with raw waveform CLDNNs,” Proc. Interspeech, pp.1-5, 2015.
3. [3] H.B. Sailor and H.A. Patil, “Novel unsupervised auditory filterbank learning using convolutional RBM for speech recognition,” IEEE Trans. Audio, Speech, Language Process., vol.24, no.12, pp.2341-2353, 2016. 10.1109/taslp.2016.2607341
4. [4] Z. Zhu, J.H. Engel, and A. Hannun, “Learning multiscale features directly from waveforms,” Proc. Interspeech, pp.1305-1309, 2016. 10.21437/interspeech.2016-256
5. [5] Z. Chen, S. Watanabe, H. Erdogan, and J.R. Hershey, “Speech enhancement and recognition using multi-task learning of long short-term memory recurrent neural networks,” Proc. Interspeech, pp.3274-3278, 2015.
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