164 citations · 226 across the 9 of their papers we have counts for
9 papers
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been w…
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…
Revealing and Protecting Labels in Distributed Training
Trung Dang, Om Thakkar, Swaroop Ramaswamy +3
Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network, thereby avoiding transmission of private data.…
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device
Zhouyuan Huo, Dongseong Hwang, Khe Chai Sim +5
Streaming end-to-end speech recognition models have been widely applied to mobile devices and show significant improvement in efficiency. These models are typically trained on the…
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
Katrin Tomanek, Françoise Beaufays, Julie Cattiau +2
While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordere…
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…