2 citations · 2 across the 4 of their papers we have counts for
6 papers
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition
Xuan Kan, Yonghui Xiao, Tien-Ju Yang +2
This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy. We emplo…
Learning from straggler clients in federated learning
Andrew Hard, Antonious M. Girgis, Ehsan Amid +4
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from…
Unintended Memorization in Large ASR Models, and How to Mitigate It
Lun Wang, Om Thakkar, Rajiv Mathews
It is well-known that neural networks can unintentionally memorize their training examples, causing privacy concerns. However, auditing memorization in large non-auto-regressive au…
Heterogeneous Federated Learning Using Knowledge Codistillation
Jared Lichtarge, Ehsan Amid, Shankar Kumar +3
Federated Averaging, and many federated learning algorithm variants which build upon it, have a limitation: all clients must share the same model architecture. This results in unus…
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…