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20192023
most citedFederated Learning for Emoji Prediction in a Mobile Keyboard

164 citations · 339 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.CL2023

Lego-Features: Exporting modular encoder features for streaming and deliberation ASR

Rami Botros, Rohit Prabhavalkar, Johan Schalkwyk +3

In end-to-end (E2E) speech recognition models, a representational tight-coupling inevitably emerges between the encoder and the decoder. We build upon recent work that has begun to…

cs.CL2023★ 113 cited

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

Yu Zhang, Wei Han, James Qin +24

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…

cs.CL2022

Handling Compounding in Mobile Keyboard Input

Andreas Kabel, Keith Hall, Tom Ouyang +3

This paper proposes a framework to improve the typing experience of mobile users in morphologically rich languages. Smartphone keyboards typically support features such as input de…

cs.CL2021★ 1 cited

A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It

Trung Dang, Om Thakkar, Swaroop Ramaswamy +3

End-to-end Automatic Speech Recognition (ASR) models are commonly trained over spoken utterances using optimization methods like Stochastic Gradient Descent (SGD). In distributed s…

cs.CL2019★ 164 cited

Federated Learning for Emoji Prediction in a Mobile Keyboard

Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao +1

We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji b…