Deep learning in speech synthesis

[1]  Dong Yu,et al.  Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  Bhuvana Ramabhadran,et al.  F0 contour prediction with a deep belief network-Gaussian process hybrid model , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Yoshihiko Nankaku,et al.  Integration of acoustic modeling and mel-cepstral analysis for HMM-based speech synthesis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Helen M. Meng,et al.  Multi-distribution deep belief network for speech synthesis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Heiga Zen,et al.  Statistical parametric speech synthesis using deep neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Dong Yu,et al.  Modeling spectral envelopes using restricted Boltzmann machines for statistical parametric speech synthesis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[9]  Heiga Zen,et al.  Product of Experts for Statistical Parametric Speech Synthesis , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Heiga Zen,et al.  Context adaptive training with factorized decision trees for HMM-based statistical parametric speech synthesis , 2011, Speech Commun..

[11]  Heiga Zen,et al.  Statistical parametric speech synthesis with joint estimation of acoustic and excitation model parameters , 2010, SSW.

[12]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[13]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[14]  Keiichi Tokuda,et al.  Statistical approach to vocal tract transfer function estimation based on factor analyzed trajectory HMM , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Frank K. Soong,et al.  Generating natural F0 trajectory with additive trees , 2008, INTERSPEECH.

[16]  Keiichi Tokuda,et al.  Minimum generation error training with direct log spectral distortion on LSPs for HMM-based speech synthesis , 2008, INTERSPEECH.

[17]  Heiga Zen,et al.  A Hidden Semi-Markov Model-Based Speech Synthesis System , 2007, IEICE Trans. Inf. Syst..

[18]  Heiga Zen,et al.  Statistical Parametric Speech Synthesis , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[19]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[20]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[21]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[22]  Keiichi Tokuda,et al.  Multi-Space Probability Distribution HMM , 2002 .

[23]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[24]  Keiichi Tokuda,et al.  Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.

[25]  Orhan Karaali,et al.  Speech Synthesis with Neural Networks , 1998, ArXiv.

[26]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .