| WaveNet: A generative model for raw audio A van den Oord, S Dieleman, H Zen, K Simonyan, O Vinyals, A Graves, ... arXiv preprint arXiv:1609.03499, 2016 | 5993 | 2016 |
| Statistical parametric speech synthesis H Zen, K Tokuda, AW Black Speech Communication 51 (11), 1039-1064, 2009 | 1560 | 2009 |
| Statistical parametric speech synthesis using deep neural networks H Zen, A Senior, M Schuster IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2013 | 1092 | 2013 |
| Parallel WaveNet: Fast high-fidelity speech synthesis A Oord, Y Li, I Babuschkin, K Simonyan, O Vinyals, K Kavukcuoglu, ... arXiv preprint arXiv:1711.10433, 2017 | 860 | 2017 |
| The HMM-based speech synthesis system (HTS) version 2.0 H Zen, T Nose, J Yamagishi, S Sako, T Masuko, AW Black, K Tokuda Sixth ISCA Workshop on Speech Synthesis (SSW6), 294-299, 2007 | 691 | 2007 |
| 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 | 563 | 2019 |
| Speech synthesis based on hidden Markov models K Tokuda, Y Nankaku, T Toda, H Zen, J Yamagishi, K Oura Proceedings of the IEEE 101 (5), 1234--1252, 2013 | 552 | 2013 |
| An HMM-based speech synthesis system applied to English K Tokuda, H Zen, AW Black IEEE Workshop on Speech Synthesis, 227-230, 2002 | 507 | 2002 |
| Wavegrad: Estimating gradients for waveform generation N Chen, Y Zhang, H Zen, RJ Weiss, M Norouzi, W Chan arXiv preprint arXiv:2009.00713, 2020 | 414 | 2020 |
| Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis H Zen, H Sak 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 360 | 2015 |
| A hidden semi-Markov model-based speech synthesis system H Zen, K Tokuda, T Masuko, T Kobayasih, T Kitamura IEICE TRANSACTIONS on Information and Systems 90 (5), 825-834, 2007 | 313 | 2007 |
| Statistical parametric speech synthesis AW Black, H Zen, K Tokuda IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2007 | 304 | 2007 |
| Details of the Nitech HMM-based speech synthesis system for the Blizzard Challenge 2005 H Zen, T Toda, M Nakamura, K Tokuda IEICE TRANSACTIONS on Information and Systems 90 (1), 325-333, 2007 | 301 | 2007 |
| Deep learning for acoustic modeling in parametric speech generation: A systematic review of existing techniques and future trends ZH Ling, SY Kang, H Zen, A Senior, M Schuster, XJ Qian, HM Meng, ... IEEE Signal Processing Magazine 32 (3), 35-52, 2015 | 279 | 2015 |
| Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis H Zen, A Senior 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 259 | 2014 |
| 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 | 246 | 2018 |
| Robust speaker-adaptive HMM-based text-to-speech synthesis J Yamagishi, T Nose, H Zen, ZH Ling, T Toda, K Tokuda, S King, S Renals IEEE Transactions on Audio, Speech, and Language Processing 17 (6), 1208-1230, 2009 | 244 | 2009 |
| Hidden semi-Markov model based speech synthesis H Zen, K Tokuda, T Masuko, T Kobayashi, T Kitamura Eighth International Conference on Spoken Language Processing (ICSLP), 1397-1400, 2004 | 200 | 2004 |
| Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences H Zen, K Tokuda, T Kitamura Computer Speech & Language 21 (1), 153-173, 2007 | 190 | 2007 |
| Reformulating HMM as a Trajectory Model by Imposing Explicit Relationships between Static and Dynamic Features H Zen Nagoya Institute of Technology, 2006 | 190* | 2006 |
Keiichi TokudaNagoya Institute of TechnologyVerified email at nitech.ac.jp
