On the training aspects of Deep Neural Network (DNN) for parametric TTS synthesis
暂无分享,去创建一个
Frank K. Soong | Wenping Hu | Yao Qian | Yuchen Fan | F. Soong | Yuchen Fan | Yao Qian | Wenping Hu
[1] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[2] Anders Krogh,et al. A Simple Weight Decay Can Improve Generalization , 1991, NIPS.
[3] Michael Picheny,et al. New methods in continuous Mandarin speech recognition , 1997, EUROSPEECH.
[4] Keiichi Tokuda,et al. Speech parameter generation algorithms for HMM-based speech synthesis , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[5] Koichi Shinoda,et al. MDL-based context-dependent subword modeling for speech recognition , 2000 .
[6] Ren-Hua Wang,et al. Minimum Generation Error Training for HMM-Based Speech Synthesis , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[7] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[8] Ren-Hua Wang,et al. USTC System for Blizzard Challenge 2006 an Improved HMM-based Speech Synthesis Method , 2006 .
[9] Heiga Zen,et al. Statistical Parametric Speech Synthesis , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[10] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[11] Dong Yu,et al. Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition , 2010 .
[12] Dong Yu,et al. Conversational Speech Transcription Using Context-Dependent Deep Neural Networks , 2012, ICML.
[13] Dong Yu,et al. Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.
[14] Tara N. Sainath,et al. Making Deep Belief Networks effective for large vocabulary continuous speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.
[15] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[16] Dong Yu,et al. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[17] S. King,et al. Combining a vector space representation of linguistic context with a deep neural network for text-to-speech synthesis , 2013, SSW.
[18] Heiga Zen,et al. Statistical parametric speech synthesis using deep neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[19] Helen M. Meng,et al. Multi-distribution deep belief network for speech synthesis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[20] 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.