| 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 | 3384 | 2016 |
| Statistical parametric speech synthesis H Zen, K Tokuda, AW Black Speech Communication 51 (11), 1039-1064, 2009 | 1369 | 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 | 901 | 2013 |
| 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 | 635 | 2007 |
| 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 | 539 | 2017 |
| An HMM-based speech synthesis system applied to English K Tokuda, H Zen, AW Black IEEE Workshop on Speech Synthesis, 227-230, 2002 | 487 | 2002 |
| 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 | 472 | 2013 |
| 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 | 302 | 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 | 294 | 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 | 287 | 2007 |
| Statistical parametric speech synthesis AW Black, H Zen, K Tokuda IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2007 | 270 | 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 | 240 | 2015 |
| 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 | 231 | 2009 |
| 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 | 218 | 2014 |
| 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 | 191 | 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 | 188 | 2007 |
| Reformulating HMM as a Trajectory Model by Imposing Explicit Relationships between Static and Dynamic Features H Zen Nagoya Institute of Technology, 2006 | 188* | 2006 |
| 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 | 170 | 2019 |
| An HMM-based singing voice synthesis system K Saino, H Zen, Y Nankaku, A Lee, K Tokuda Ninth International Conference on Spoken Language Processing (ICSLP), 1141-1144, 2006 | 141 | 2006 |
| The Festival Speech Synthesis System Version 1.4.2 A Black, P Taylor, R Caley, R Clark, K Richmond, S King, V Strom, H Zen | 131* | 2001 |
Keiichi TokudaNagoya Institute of TechnologyVerified email at nitech.ac.jp
