Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context
暂无分享,去创建一个
Yan Li | Jie Li | Xiaorui Wang | Yahui Shan
[1] 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.
[2] Zhijie Yan,et al. Improving latency-controlled BLSTM acoustic models for online speech recognition , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[3] Shiliang Zhang,et al. Compact Feedforward Sequential Memory Networks for Large Vocabulary Continuous Speech Recognition , 2016, INTERSPEECH.
[4] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[5] Ying Zhang,et al. Batch normalized recurrent neural networks , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[6] Yiming Wang,et al. Low Latency Acoustic Modeling Using Temporal Convolution and LSTMs , 2018, IEEE Signal Processing Letters.
[7] Aaron C. Courville,et al. Recurrent Batch Normalization , 2016, ICLR.
[8] Hermann Ney,et al. Towards Online-Recognition with Deep Bidirectional LSTM Acoustic Models , 2016, INTERSPEECH.
[9] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[10] Kai Chen,et al. Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[11] Sanjeev Khudanpur,et al. Audio augmentation for speech recognition , 2015, INTERSPEECH.
[12] Jürgen Schmidhuber,et al. Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[13] Shiliang Zhang,et al. Deep-FSMN for Large Vocabulary Continuous Speech Recognition , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[14] Navdeep Jaitly,et al. Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[15] Yu Zhang,et al. Highway long short-term memory RNNS for distant speech recognition , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[16] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[17] Sanjeev Khudanpur,et al. A time delay neural network architecture for efficient modeling of long temporal contexts , 2015, INTERSPEECH.
[18] Yoshua Bengio,et al. Light Gated Recurrent Units for Speech Recognition , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.
[19] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[20] Jürgen Schmidhuber,et al. Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition , 2005, ICANN.
[21] Sanjeev Khudanpur,et al. A study on data augmentation of reverberant speech for robust speech recognition , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Hao Zheng,et al. AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline , 2017, 2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA).
[23] Dong Wang,et al. THCHS-30 : A Free Chinese Speech Corpus , 2015, ArXiv.
[24] Yiming Wang,et al. Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI , 2016, INTERSPEECH.
[25] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.
[26] Jie Li,et al. Gated Recurrent Unit Based Acoustic Modeling with Future Context , 2018, INTERSPEECH.
[27] Yu Hu,et al. Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency , 2015, ArXiv.
[28] Zhi-Jie Yan,et al. A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).
[29] Yoshua Bengio,et al. Improving Speech Recognition by Revising Gated Recurrent Units , 2017, INTERSPEECH.
[30] Jürgen Schmidhuber,et al. Learning to forget: continual prediction with LSTM , 1999 .
[31] Andrew W. Senior,et al. Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition , 2014, ArXiv.