An Investigation of Phone-Based Subword Units for End-to-End Speech Recognition
暂无分享,去创建一个
[1] John J. Godfrey,et al. SWITCHBOARD: telephone speech corpus for research and development , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[2] Rico Sennrich,et al. Neural Machine Translation of Rare Words with Subword Units , 2015, ACL.
[3] Gabriel Synnaeve,et al. Wav2Letter: an End-to-End ConvNet-based Speech Recognition System , 2016, ArXiv.
[4] Jan Niehues,et al. Very Deep Self-Attention Networks for End-to-End Speech Recognition , 2019, INTERSPEECH.
[5] Quoc V. Le,et al. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[6] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[7] Liang Lu,et al. Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition , 2017, INTERSPEECH.
[8] Karen Livescu,et al. Jointly learning to align and convert graphemes to phonemes with neural attention models , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).
[9] Fuchun Peng,et al. Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[10] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[11] Shinji Watanabe,et al. ESPnet: End-to-End Speech Processing Toolkit , 2018, INTERSPEECH.
[12] James Glass,et al. Subword Regularization and Beam Search Decoding for End-to-end Automatic Speech Recognition , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] John R. Hershey,et al. Hybrid CTC/Attention Architecture for End-to-End Speech Recognition , 2017, IEEE Journal of Selected Topics in Signal Processing.
[14] Kyu J. Han,et al. The CAPIO 2017 Conversational Speech Recognition System , 2017, ArXiv.
[15] Shinji Watanabe,et al. Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[16] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[17] Chong Wang,et al. Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.
[18] Chengzhu Yu,et al. A Multistage Training Framework for Acoustic-to-Word Model , 2018, INTERSPEECH.
[19] Rohit Prabhavalkar,et al. Exploring architectures, data and units for streaming end-to-end speech recognition with RNN-transducer , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[20] Hermann Ney,et al. Improved training of end-to-end attention models for speech recognition , 2018, INTERSPEECH.
[21] Taku Kudo,et al. Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates , 2018, ACL.
[22] Hermann Ney,et al. Joint-sequence models for grapheme-to-phoneme conversion , 2008, Speech Commun..
[23] Mike Schuster,et al. Japanese and Korean voice search , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[24] Daniel Povey,et al. The Kaldi Speech Recognition Toolkit , 2011 .
[25] Tara N. Sainath,et al. Streaming End-to-end Speech Recognition for Mobile Devices , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[26] Yajie Miao,et al. EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).
[27] Nanyun Peng,et al. Espresso: A Fast End-to-End Neural Speech Recognition Toolkit , 2019, 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[28] Andrew W. Senior,et al. Fast and accurate recurrent neural network acoustic models for speech recognition , 2015, INTERSPEECH.
[29] Quoc V. Le,et al. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition , 2019, INTERSPEECH.
[30] John R. Hershey,et al. Multi-level language modeling and decoding for open vocabulary end-to-end speech recognition , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[31] Jun Wang,et al. Improving Attention-Based End-to-End ASR Systems with Sequence-Based Loss Functions , 2018, 2018 IEEE Spoken Language Technology Workshop (SLT).
[32] Yoshua Bengio,et al. On Using Monolingual Corpora in Neural Machine Translation , 2015, ArXiv.
[33] S. J. Young,et al. Tree-based state tying for high accuracy acoustic modelling , 1994 .
[34] Janet M. Baker,et al. The Design for the Wall Street Journal-based CSR Corpus , 1992, HLT.
[35] Géza Németh,et al. Transformer based Grapheme-to-Phoneme Conversion , 2019, INTERSPEECH.
[36] Hasim Sak,et al. Multi-accent speech recognition with hierarchical grapheme based models , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[37] Florian Metze,et al. An empirical exploration of CTC acoustic models , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[38] Xiaofei Wang,et al. A Comparative Study on Transformer vs RNN in Speech Applications , 2019, 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[39] Hermann Ney,et al. A comprehensive analysis on attention models , 2019, NeurIPS 2019.
[40] Shinji Watanabe,et al. Promising Accurate Prefix Boosting for Sequence-to-sequence ASR , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).