Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition
暂无分享,去创建一个
Yongqiang Wang | Mark J. F. Gales | Xunying Liu | Philip C. Woodland | Xie Chen | Xie Chen | P. Woodland | M. Gales | Yongqiang Wang | Xunying Liu
[1] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[2] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[3] Andreas Stolcke,et al. SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.
[4] Andreas Stolcke,et al. Getting More Mileage from Web Text Sources for Conversational Speech Language Modeling using Class-Dependent Mixtures , 2003, NAACL.
[5] H. Schwenk,et al. Efficient training of large neural networks for language modeling , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[6] Mark J. F. Gales,et al. Training LVCSR systems on thousands of hours of data , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[7] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[8] Holger Schwenk,et al. Continuous space language models , 2007, Comput. Speech Lang..
[9] Ahmad Emami,et al. Empirical study of neural network language models for Arabic speech recognition , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).
[10] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[11] Mark J. F. Gales,et al. Improved neural network based language modelling and adaptation , 2010, INTERSPEECH.
[12] Lukás Burget,et al. Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] Dong Yu,et al. Conversational Speech Transcription Using Context-Dependent Deep Neural Networks , 2012, ICML.
[14] Tomas Mikolov,et al. RNNLM - Recurrent Neural Network Language Modeling Toolkit , 2011 .
[15] Sanjeev Khudanpur,et al. Variational approximation of long-span language models for lvcsr , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[16] Aapo Hyvärinen,et al. Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics , 2012, J. Mach. Learn. Res..
[17] Yee Whye Teh,et al. A fast and simple algorithm for training neural probabilistic language models , 2012, ICML.
[18] Dong Yu,et al. Pipelined BackPropagation for Context-Dependent Deep Neural Networks , 2012 .
[19] Petr Motlícek,et al. Conversion of Recurrent Neural Network Language Models to Weighted Finite State Transducers for Automatic Speech Recognition , 2012, INTERSPEECH.
[20] Dong Yu,et al. Pipelined Back-Propagation for Context-Dependent Deep Neural Networks , 2012, INTERSPEECH.
[21] Ebru Arisoy,et al. Large Scale Hierarchical Neural Network Language Models , 2012, INTERSPEECH.
[22] Hermann Ney,et al. LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.
[23] Geoffrey Zweig,et al. Accelerating recurrent neural network training via two stage classes and parallelization , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[24] Geoffrey Zweig,et al. Joint Language and Translation Modeling with Recurrent Neural Networks , 2013, EMNLP.
[25] Geoffrey Zweig,et al. Speed regularization and optimality in word classing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[26] Geoffrey Zweig,et al. Recurrent neural networks for language understanding , 2013, INTERSPEECH.
[27] Yonghong Yan,et al. Prefix tree based n-best list re-scoring for recurrent neural network language model used in speech recognition system , 2013, INTERSPEECH.
[28] Ashish Vaswani,et al. Decoding with Large-Scale Neural Language Models Improves Translation , 2013, EMNLP.
[29] Yangyang Shi,et al. Speed up of recurrent neural network language models with sentence independent subsampling stochastic gradient descent , 2013, INTERSPEECH.
[30] Phil Blunsom,et al. Recurrent Continuous Translation Models , 2013, EMNLP.
[31] Kenneth Ward Church,et al. Approximate inference: A sampling based modeling technique to capture complex dependencies in a language model , 2012, Speech Commun..
[32] Alexandre Allauzen,et al. Structured Output Layer Neural Network Language Models for Speech Recognition , 2013, IEEE Transactions on Audio, Speech, and Language Processing.
[33] Yoshua Bengio,et al. Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding , 2013, INTERSPEECH.
[34] Weiqiang Zhang,et al. Temporal kernel neural network language model , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[35] Hermann Ney,et al. Comparison of feedforward and recurrent neural network language models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[36] Richard M. Schwartz,et al. Fast and Robust Neural Network Joint Models for Statistical Machine Translation , 2014, ACL.
[37] Yongqiang Wang,et al. Efficient GPU-based training of recurrent neural network language models using spliced sentence bunch , 2014, INTERSPEECH.
[38] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[39] Atsushi Nakamura,et al. Real-time one-pass decoding with recurrent neural network language model for speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[40] Geoffrey Zweig,et al. An introduction to computational networks and the computational network toolkit (invited talk) , 2014, INTERSPEECH.
[41] Thorsten Brants,et al. One billion word benchmark for measuring progress in statistical language modeling , 2013, INTERSPEECH.
[42] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[43] Meng Cai,et al. Efficient One-Pass Decoding with NNLM for Speech Recognition , 2014, IEEE Signal Processing Letters.
[44] Geoffrey Zweig,et al. Cache based recurrent neural network language model inference for first pass speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[45] Dong Yu,et al. On parallelizability of stochastic gradient descent for speech DNNS , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[46] Meng Cai,et al. Variance regularization of RNNLM for speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[47] Yongqiang Wang,et al. Efficient lattice rescoring using recurrent neural network language models , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[48] Mark J. F. Gales,et al. Paraphrastic recurrent neural network language models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[49] Mark J. F. Gales,et al. Improving the training and evaluation efficiency of recurrent neural network language models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[50] Mark J. F. Gales,et al. Recurrent neural network language model training with noise contrastive estimation for speech recognition , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[51] Hermann Ney,et al. From Feedforward to Recurrent LSTM Neural Networks for Language Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[52] Tony Robinson,et al. Scaling recurrent neural network language models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[53] Ebru Arisoy,et al. Unnormalized exponential and neural network language models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[54] Mark J. F. Gales,et al. The Cambridge University 2014 BOLT conversational telephone Mandarin Chinese LVCSR system for speech translation , 2015, INTERSPEECH.
[55] Marie-Francine Moens,et al. A survey on the application of recurrent neural networks to statistical language modeling , 2015, Comput. Speech Lang..
[56] Mark J. F. Gales,et al. Recurrent neural network language model adaptation for multi-genre broadcast speech recognition , 2015, INTERSPEECH.
[57] Mark J. F. Gales,et al. CUED-RNNLM — An open-source toolkit for efficient training and evaluation of recurrent neural network language models , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).