Yu Zhang
Yu Zhang
Verified email at csail.mit.edu - Homepage
Cited by
Cited by
Natural tts synthesis by conditioning wavenet on mel spectrogram predictions
J Shen, R Pang, RJ Weiss, M Schuster, N Jaitly, Z Yang, Z Chen, Y Zhang, ...
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
An introduction to computational networks and the computational network toolkit
MS Dong Yu, Adam Eversole, Mike Seltzer, Kaisheng Yao, Zhiheng Huang, Brian ...
Tech. Rep. MSR, Microsoft Research, 2014, http://codebox/cntk, 2014
Specaugment: A simple data augmentation method for automatic speech recognition
DS Park, W Chan, Y Zhang, CC Chiu, B Zoph, ED Cubuk, QV Le
arXiv preprint arXiv:1904.08779, 2019
Very deep convolutional networks for end-to-end speech recognition
Y Zhang, W Chan, N Jaitly
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
Spoken language understanding using long short-term memory neural networks
K Yao, B Peng, Y Zhang, D Yu, G Zweig, Y Shi
IEEE SLT, 2014
Highway long short-term memory rnns for distant speech recognition
Y Zhang, G Chen, D Yu, K Yaco, S Khudanpur, J Glass
2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016
Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis
Y Wang, D Stanton, Y Zhang, RJ Skerry-Ryan, E Battenberg, J Shor, ...
arXiv preprint arXiv:1803.09017, 2018
Transfer learning from speaker verification to multispeaker text-to-speech synthesis
Y Jia, Y Zhang, R Weiss, Q Wang, J Shen, F Ren, P Nguyen, R Pang, ...
Advances in neural information processing systems, 4480-4490, 2018
Unsupervised learning of disentangled and interpretable representations from sequential data
WN Hsu, Y Zhang, J Glass
Advances in neural information processing systems, 1878-1889, 2017
Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM
T Hori, S Watanabe, Y Zhang, W Chan
arXiv preprint arXiv:1706.02737, 2017
Training rnns as fast as cnns
T Lei, Y Zhang, Y Artzi
Deep beamforming networks for multi-channel speech recognition
X Xiao, S Watanabe, H Erdogan, L Lu, J Hershey, ML Seltzer, G Chen, ...
2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016
Simple recurrent units for highly parallelizable recurrence
T Lei, Y Zhang, SI Wang, H Dai, Y Artzi
arXiv preprint arXiv:1709.02755, 2017
I-Vector Based Clustering Training Data in Speech Recognition
Q Huo, ZJ Yan, Y Zhang, J Xu
US Patent App. 13/640,804, 2015
Learning latent representations for speech generation and transformation
WN Hsu, Y Zhang, J Glass
arXiv preprint arXiv:1704.04222, 2017
LibriTTS: A corpus derived from LibriSpeech for text-to-speech
H Zen, V Dang, R Clark, Y Zhang, RJ Weiss, Y Jia, Z Chen, Y Wu
arXiv preprint arXiv:1904.02882, 2019
Extracting deep neural network bottleneck features using low-rank matrix factorization
Y Zhang, E Chuangsuwanich, J Glass
2014 IEEE international conference on acoustics, speech and signal …, 2014
Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation
WN Hsu, Y Zhang, J Glass
2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 16-23, 2017
Hierarchical generative modeling for controllable speech synthesis
WN Hsu, Y Zhang, RJ Weiss, H Zen, Y Wu, Y Wang, Y Cao, Y Jia, Z Chen, ...
arXiv preprint arXiv:1810.07217, 2018
Lingvo: a modular and scalable framework for sequence-to-sequence modeling
J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ...
arXiv preprint arXiv:1902.08295, 2019
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