Heiga Zen
- Research Area(s)
- Machine Intelligence
- Speech Processing
Co-Authors
Google Publications
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Directly Modeling Voiced and Unvoiced Components in Speech Waveforms by Neural Networks
Keiichi Tokuda, Heiga Zen
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2016), pp. 5640-5644
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Heiga Zen, Yannis Agiomyrgiannakis, Niels Egberts, Fergus Henderson, Przemysław Szczepaniak
Proc. Interspeech, San Francisco, CA, USA (2016) (to appear)
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Proc. Interspeech, ISCA (2016) (to appear)
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Acoustic Modeling for Speech Synthesis: from HMM to RNN
IEEE ASRU, Scottsdale, Arizona, U.S.A. (2015)
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Acoustic Modeling in Statistical Parametric Speech Synthesis - From HMM to LSTM-RNN
Proc. MLSLP (2015)
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Zhen-Hua Ling, Shiyin Kang, Heiga Zen, Andrew Senior, Mike Schuster, Xiao-Jun Qian, Helen Meng, Li Deng
IEEE Signal Processing Magazine, vol. 32 (2015), pp. 35-52
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Directly Modeling Speech Waveforms by Neural Networks for Statistical Parametric Speech Synthesis
Keiichi Tokuda, Heiga Zen
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2015), pp. 4215-4219
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Statistical parametric speech synthesis: from HMM to LSTM-RNN
RTTH Summer School on Speech Technology -- A Deep Learning Perspective, Barcelona, Spain (2015)
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2015), pp. 4470-4474
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Deep Mixture Density Networks for Acoustic Modeling in Statistical Parametric Speech Synthesis
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2014), pp. 3872-3876
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Statistical Parametric Speech Synthesis
UKSpeech Conference, Edinburgh, UK (2014)
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Deep Learning in Speech Synthesis
8th ISCA Speech Synthesis Workshop, Barcelona, Spain (2013)
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Statistical Parametric Speech Synthesis Using Deep Neural Networks
Heiga Zen, Andrew Senior, Mike Schuster
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2013), pp. 7962-7966
Previous Publications
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Product of Experts for Statistical Parametric Speech Synthesis
Heiga Zen, Mark J. F. Gales, Yoshihiko Nankaku, Keiichi Tokuda
IEEE Transactions on Audio, Speech, and Language Processing, vol. 20 (2012), pp. 794-805
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Statistical Parametric Speech Synthesis Based on Speaker and Language Factorization
Heiga Zen, Norbert Braunschweiler, Sabine Buchholz, Mark J. F. Gales, Kate Knill, Sacha Krstulovic, Javier Latorre
IEEE Transactions on Audio, Speech, and Language Processing, vol. 20 (2012), pp. 1713-1724
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Continuous Stochastic Feature Mapping Based on Trajectory HMMs
Heiga Zen, Yoshihiko Nankaku, Keiichi Tokuda
IEEE Transactions on Audio, Speech, and Language Processing, vol. 19 (2011), pp. 417-430
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The HMM-Based Speech Synthesis System (HTS)
Heiga Zen, Keiichi Tokuda
Computer Processing of Asian Spoken Languages, Americas Group Publications (2010)
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Statistical Parametric Speech Synthesis
Heiga Zen, Keiichi Tokuda, Alan W. Black
Speech Communication, vol. 51 (2009), pp. 1039-1064
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The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006
Heiga Zen, Tomoki Toda, Keiichi Tokuda
IEICE Transactions on Information and Systems, vol. E91-D (2008), pp. 1764-1773
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A Hidden Semi-Markov Model-Based Speech Synthesis System
Heiga Zen, Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi Kitamura
IEICE Transactions on Information and Systems, vol. E90-D (2007), pp. 825-834
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Details of Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005
Heiga Zen, Tomoki Toda, Masaru Nakamura, Keiichi Tokuda
IEICE Transactions on Information and Systems, vol. E90-D (2007), pp. 325-333
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Reformulating the HMM as a Trajectory Model by Imposing Explicit Relationships Between Static and Dynamic Feature Vector Sequences
Heiga Zen, Keiichi Tokuda, Tadashi Kitamura
Computer Speech and Language, vol. 21 (2007), pp. 153-173
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HMM-Based Approach to Multilingual Speech Synthesis
Keiichi Tokuda, Heiga Zen, Alan W. Black
Text to Speech Synthesis: New Paradigms and Advances, Prentice Hall (2004)






