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The transducer architecture is becoming increasingly popular in the field of speech recognition, because it is naturally streaming as well as high in accuracy.
“Sequence transduction with recurrent neural networks,”
Alex Graves, · 2012
Earlier work this paper cites.
“Librispeech: An asr corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“MUSAN: A Music, Speech, and Noise Corpus,” 2015,
David Snyder, Guoguo Chen, and Daniel Povey, · 2015
Earlier work this paper cites.
“Audio augmentation for speech recognition,”
Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“ESPnet: End-to-end speech processing toolkit,”
Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, and Tsubasa Ochiai, · 2018
Earlier work this paper cites.
“Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,”
Taku Kudo and John Richardson, · 2018
Cited alongside, same era.
“Streaming end-to-end speech recognition for mobile devices,”
Yanzhang He, Tara N Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, et al., · 2019
Cited alongside, same era.
“Rnn-t for latency controlled asr with improved beam search,”
Mahaveer Jain, Kjell Schubert, Jay Mahadeokar, Ching-Feng Yeh, Kaustubh Kalgaonkar, Anuroop Sriram, Christian Fuegen, and Michael L Seltzer, · 2019
Cited alongside, same era.
“Monotonic recurrent neural network transducer and decoding strategies,”
Anshuman Tripathi, Han Lu, Hasim Sak, and Hagen Soltau, · 2019
Cited alongside, same era.
“Specaugment: A simple data augmentation method for automatic speech recognition,”
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le, · 2019
“Accelerating rnn transducer inference via one-step constrained beam search,”
Juntae Kim and Yoonhan Lee, · 2020
Later among the works it cites.
“Rnn-transducer with stateless prediction network,”
Mohammadreza Ghodsi, Xiaofeng Liu, James Apfel, Rodrigo Cabrera, and Eugene Weinstein, · 2020
Later among the works it cites.
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati, Chung-Cheng Chiu, James Qin, Jiahui Yu, Niki Parmar, Ruoming Pang, Shibo Wang, Wei Han, Yonghui Wu, Yu Zhang, and Zhengdong Zhang, Eds., · 2020
Later among the works it cites.
“Less is more: Improved rnn-t decoding using limited label context and path merging,”
Rohit Prabhavalkar, Yanzhang He, David Rybach, Sean Campbell, Arun Narayanan, Trevor Strohman, and Tara N Sainath, · 2021
Later among the works it cites.
Lhotse: a speech data representation library for the modern deep learning ecosystem
2021
Later among the works it cites.
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Cited alongside, same era.
“A streaming on-device end-to-end model surpassing server-side conventional model quality and latency,”
Tara N Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, et al., · 2020
Cited alongside, same era.
“Pruned RNN-T for fast, memory-efficient ASR training,”
Fangjun Kuang, Liyong Guo, Wei Kang, Long Lin, Mingshuang Luo, Zengwei Yao, and Daniel Povey, · 2072
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