| 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 | 4092 | 2019 |
| 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 | 3234 | 2018 |
| Conformer: Convolution-augmented transformer for speech recognition A Gulati, J Qin, CC Chiu, N Parmar, Y Zhang, J Yu, W Han, S Wang, ... arXiv preprint arXiv:2005.08100, 2020 | 3117 | 2020 |
| 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 31, 2018 | 968 | 2018 |
| Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis Y Wang, D Stanton, Y Zhang, RJS Ryan, E Battenberg, J Shor, Y Xiao, ... International conference on machine learning, 5180-5189, 2018 | 962 | 2018 |
| 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 | 886 | 2019 |
| Wavegrad: Estimating gradients for waveform generation N Chen, Y Zhang, H Zen, RJ Weiss, M Norouzi, W Chan arXiv preprint arXiv:2009.00713, 2020 | 740 | 2020 |
| 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 | 562 | 2017 |
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| Unsupervised learning of disentangled and interpretable representations from sequential data WN Hsu, Y Zhang, J Glass Advances in neural information processing systems 30, 2017 | 422 | 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 | 406 | 2014 |
| W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training YA Chung, Y Zhang, W Han, CC Chiu, J Qin, R Pang, Y Wu 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2021 | 383 | 2021 |
| ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech X Wang, J Yamagishi, M Todisco, H Delgado, A Nautsch, N Evans, ... Computer Speech & Language 64, 101114, 2020 | 371 | 2020 |
| Highway long short-term memory rnns for distant speech recognition Y Zhang, G Chen, D Yu, K Yao, S Khudanpur, J Glass 2016 IEEE international conference on acoustics, speech and signal …, 2016 | 364 | 2016 |
| Pushing the limits of semi-supervised learning for automatic speech recognition Y Zhang, J Qin, DS Park, W Han, CC Chiu, R Pang, QV Le, Y Wu arXiv preprint arXiv:2010.10504, 2020 | 363 | 2020 |
| 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 | 356 | 2017 |
| Simple recurrent units for highly parallelizable recurrence T Lei, Y Zhang, SI Wang, H Dai, Y Artzi arXiv preprint arXiv:1709.02755, 2017 | 333 | 2017 |
| Contextnet: Improving convolutional neural networks for automatic speech recognition with global context W Han, Z Zhang, Y Zhang, J Yu, CC Chiu, J Qin, A Gulati, R Pang, Y Wu arXiv preprint arXiv:2005.03191, 2020 | 307 | 2020 |
| 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 | 276 | 2018 |
| Improved noisy student training for automatic speech recognition DS Park, Y Zhang, Y Jia, W Han, CC Chiu, B Li, Y Wu, QV Le arXiv preprint arXiv:2005.09629, 2020 | 265 | 2020 |
Yonghui WuGoogle BrainE-mail megerősítve itt: google.com
