Heiga Zen
Heiga Zen
Principal Scientist (Director), Google DeepMind
Verified email at - Homepage
Cited by
Cited by
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
Statistical parametric speech synthesis
H Zen, K Tokuda, AW Black
Speech Communication 51 (11), 1039-1064, 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
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
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
Interspeech, 1526-1530, 2019
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
Wavegrad: Estimating gradients for waveform generation
N Chen, Y Zhang, H Zen, RJ Weiss, M Norouzi, W Chan
arXiv preprint arXiv:2009.00713, 2020
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
An HMM-based speech synthesis system applied to English
K Tokuda, H Zen, AW Black
IEEE Workshop on Speech Synthesis, 227-230, 2002
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
Statistical parametric speech synthesis
AW Black, H Zen, K Tokuda
IEEE International Conference on Acoustics, Speech, and Signal Processing†…, 2007
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
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
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
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
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
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
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
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
Reformulating HMM as a Trajectory Model by Imposing Explicit Relationships between Static and Dynamic Features
H Zen
Nagoya Institute of Technology, 2006
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