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In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model.
“Bidirectional recurrent neural networks,”
M. Schuster and K. K. Paliwal, · 1997
Earlier work this paper cites.
“Bidirectional lstm networks for improved phoneme classification and recognition,”
Alex Graves, Santiago Fernández, and Jürgen Schmidhuber, · 2005
Earlier work this paper cites.
“Sequence transduction with recurrent neural networks,”
Alex Graves, · 2012
Earlier work this paper cites.
“Learning acoustic frame labeling for speech recognition with recurrent neural networks,”
H. Sak, A. Senior, K. Rao, O. İrsoy, A. Graves, F. Beaufays, and J. Schalkwyk, · 2015
Earlier work this paper cites.
“Acoustic modelling with cd-ctc-smbr lstm rnns,”
A. Senior, H. Sak, F. de Chaumont Quitry, T. Sainath, and K. Rao, · 2015
Cited alongside, same era.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Cited alongside, same era.
“In-datacenter performance analysis of a tensor processing unit,”
Jouppi et al, · 2017
Cited alongside, same era.
“Transformer-xl: Attentive language models beyond a fixed-length context,”
Zihang Dai, Zhilin Yang, Yiming Yang, William W Cohen, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov, · 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
Later among the works it cites.
“Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss,” 2020
Qian Zhang, Han Lu, Hasim Sak, Anshuman Tripathi, Erik McDermott, Stephen Koo, and Shankar Kumar, · 2020
Closest in time.
“Rnn-transducer with stateless prediction network,”
M. Ghodsi, X. Liu, J. Apfel, R. Cabrera, and E. Weinstein, · 2020
Closest in time.
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