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Integer quantization of neural networks can be defined as the approximation of the high precision computation of the canonical neural network formulation, using reduced integer precision.
Long short-term memory
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A cascade architecture for keyword spotting on mobile devices
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Tacotron: A fully end-to-end text-to-speech synthesis model
Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous · 2017
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Simple recurrent units for highly parallelizable recurrence
Tao Lei, Yu Zhang, Sida I Wang, Hui Dai, and Yoav Artzi · 2018
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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
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Personalized speech recognition on mobile devices
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Optimizing speech recognition for the edge
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A streaming on-device end-to-end model surpassing server-side conventional model quality and latency
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