| Unsupervised learning of disentangled and interpretable representations from sequential data WN Hsu, Y Zhang, J Glass arXiv preprint arXiv:1709.07902, 2017 | 221 | 2017 |
| Learning Latent Representations for Speech Generation and Transformation WN Hsu, Y Zhang, J Glass INTERSPEECH, 1273-1277, 2017 | 103 | 2017 |
| An unsupervised autoregressive model for speech representation learning YA Chung, WN Hsu, H Tang, J Glass INTERSPEECH, 2019 | 93 | 2019 |
| 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, ... Seventh International Conference on Learning Representations (ICLR), 2019 | 90 | 2019 |
| 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 | 85 | 2019 |
| 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 | 84 | 2017 |
| Active learning by learning WN Hsu, HT Lin Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 71 | 2015 |
| Multi-channel speech recognition: Lstms all the way through H Erdogan, T Hayashi, JR Hershey, T Hori, C Hori, WN Hsu, S Kim, ... CHiME-4 workshop, 1-4, 2016 | 61 | 2016 |
| Semi-supervised training for improving data efficiency in end-to-end speech synthesis YA Chung, Y Wang, WN Hsu, Y Zhang, RJ Skerry-Ryan ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 60 | 2019 |
| Disentangling correlated speaker and noise for speech synthesis via data augmentation and adversarial factorization WN Hsu, Y Zhang, RJ Weiss, YA Chung, Y Wang, Y Wu, J Glass ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 51 | 2019 |
| Neural attention for learning to rank questions in community question answering S Romeo, G Da San Martino, A Barrón-Cedeno, A Moschitti, Y Belinkov, ... Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 35 | 2016 |
| Sls at semeval-2016 task 3: Neural-based approaches for ranking in community question answering M Mohtarami, Y Belinkov, WN Hsu, Y Zhang, T Lei, K Bar, S Cyphers, ... Proceedings of the 10th international workshop on semantic evaluation …, 2016 | 34 | 2016 |
| Extracting domain invariant features by unsupervised learning for robust automatic speech recognition WN Hsu, J Glass 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 30 | 2018 |
| Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech D Harwath, WN Hsu, J Glass Eighth International Conference on Learning Representations (ICLR), 2020 | 29 | 2020 |
| A prioritized grid long short-term memory RNN for speech recognition WN Hsu, Y Zhang, J Glass 2016 IEEE Spoken Language Technology Workshop (SLT), 467-473, 2016 | 25 | 2016 |
| Exploiting depth and highway connections in convolutional recurrent deep neural networks for speech recognition WN Hsu, Y Zhang, A Lee, J Glass INTERSPEECH, 2016 | 25 | 2016 |
| Scalable factorized hierarchical variational autoencoder training WN Hsu, J Glass INTERSPEECH, 2018 | 21 | 2018 |
| A study of enhancement, augmentation, and autoencoder methods for domain adaptation in distant speech recognition H Tang, WN Hsu, F Grondin, J Glass INTERSPEECH, 2018 | 17 | 2018 |
| Disentangling by partitioning: A representation learning framework for multimodal sensory data WN Hsu, J Glass arXiv preprint arXiv:1805.11264, 2018 | 16 | 2018 |
| Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, et al.,“Hierarchical generative modeling for controllable speech synthesis,” WN Hsu, Y Zhang, RJ Weiss arXiv preprint arXiv:1810.07217, 2018 | 14 | 2018 |
James GlassMIT Computer Science and Artificial Intelligence LaboratoryПотвърден имейл адрес: mit.edu
