1.1k citations · 1.6k across the 34 of their papers we have counts for
10 papers · 1 filter
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews +5
In this work, we focus on improving the accuracy-variance trade-off for state-of-the-art differentially private machine learning (DP ML) methods. First, we design a general framewo…
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning
Sandy Ritchie, You-Chi Cheng, Mingqing Chen +4
Almost none of the 2,000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages. W…
UserLibri: A Dataset for ASR Personalization Using Only Text
Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani +6
Personalization of speech models on mobile devices (on-device personalization) is an active area of research, but more often than not, mobile devices have more text-only data than…
Mixed Federated Learning: Joint Decentralized and Centralized Learning
Sean Augenstein, Andrew Hard, Lin Ning +4
Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixe…
Online Model Compression for Federated Learning with Large Models
Tien-Ju Yang, Yonghui Xiao, Giovanni Motta +3
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Onli…
Scaling Language Model Size in Cross-Device Federated Learning
Jae Hun Ro, Theresa Breiner, Lara McConnaughey +4
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage vario…
Detecting Unintended Memorization in Language-Model-Fused ASR
W. Ronny Huang, Steve Chien, Om Thakkar +1
End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words. At the same…
Extracting Targeted Training Data from ASR Models, and How to Mitigate It
Ehsan Amid, Om Thakkar, Arun Narayanan +2
Recent work has designed methods to demonstrate that model updates in ASR training can leak potentially sensitive attributes of the utterances used in computing the updates. In thi…
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training
Andrew Hard, Kurt Partridge, Neng Chen +9
We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To comp…
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Hao Zhang, You-Chi Cheng, Shankar Kumar +3
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierar…