End-to-end models with large capacity have significantly improved
multil...
In end-to-end (E2E) speech recognition models, a representational
tight-...
We introduce the Universal Speech Model (USM), a single large model that...
Automatic Speech Recognition models require large amount of speech data ...
This paper addresses the challenges of training large neural network mod...
Recent work has designed methods to demonstrate that model updates in AS...
We trained a keyword spotting model using federated learning on real use...
This paper proposes a framework to improve the typing experience of mobi...
Distributed learning paradigms such as federated learning often involve
...
This paper aims to address the major challenges of Federated Learning (F...
Transformer-based architectures have been the subject of research aimed ...
Federated learning can be used to train machine learning models on the e...
Fast contextual adaptation has shown to be effective in improving Automa...
Self- and semi-supervised learning methods have been actively investigat...
Streaming end-to-end speech recognition models have been widely applied ...
We summarize the results of a host of efforts using giant automatic spee...
While current state-of-the-art Automatic Speech Recognition (ASR) system...
End-to-end Automatic Speech Recognition (ASR) models are commonly traine...
We propose using federated learning, a decentralized on-device learning
...
This paper presents the first consumer-scale next-word prediction (NWP) ...
Recent works have shown that generative sequence models (e.g., language
...
The demand for fast and accurate incremental speech recognition increase...
Training machine learning models on mobile devices has the potential of
...
We study the effectiveness of several techniques to personalize end-to-e...
This technical report describes our deep internationalization program fo...
Federated learning is a distributed, on-device computation framework tha...
We propose algorithms to train production-quality n-gram language models...
Speaker-independent speech recognition systems trained with data from ma...
We show that a word-level recurrent neural network can predict emoji fro...
We demonstrate that a character-level recurrent neural network is able t...
Federated learning is a distributed form of machine learning where both ...
We train a recurrent neural network language model using a distributed,
...
We propose a finite-state transducer (FST) representation for the models...
We describe a large vocabulary speech recognition system that is accurat...
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent...
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN)
archit...