Towards end-to-end speech recognition for Chinese Mandarin using long short-term memory recurrent neural networks
暂无分享,去创建一个
Jie Li | Heng Zhang | Bo Xu | Xinyuan Cai
[1] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[2] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[3] Rong Zheng,et al. Asynchronous stochastic gradient descent for DNN training , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[4] Daniel Jurafsky,et al. First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs , 2014, ArXiv.
[5] Thomas Hain,et al. Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition , 2006, INTERSPEECH.
[6] Tara N. Sainath,et al. Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization , 2012, INTERSPEECH.
[7] 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.
[8] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[9] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[10] Dong Yu,et al. Error back propagation for sequence training of Context-Dependent Deep NetworkS for conversational speech transcription , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[11] Yves Normandin. Maximum Mutual Information Estimation of Hidden Markov Models , 1996 .
[12] Xiangang Li,et al. Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] Alex Graves,et al. Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.
[14] Georg Heigold,et al. Sequence discriminative distributed training of long short-term memory recurrent neural networks , 2014, INTERSPEECH.
[15] Lukás Burget,et al. Sequence-discriminative training of deep neural networks , 2013, INTERSPEECH.
[16] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[17] 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.
[18] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[19] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[20] Jing Peng,et al. An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories , 1990, Neural Computation.
[21] Andrew W. Senior,et al. Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.
[22] Brian Kingsbury,et al. Boosted MMI for model and feature-space discriminative training , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[23] Andrew W. Senior,et al. Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition , 2014, ArXiv.
[24] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[25] Jürgen Schmidhuber,et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.