5 citations · 6 across the 4 of their papers we have counts for
4 papers
Federated Learning of Large ASR Models in the Real World
Yonghui Xiao, Yuxin Ding, Changwan Ryu +2
Federated learning (FL) has shown promising results on training machine learning models with privacy preservation. However, for large models with over 100 million parameters, the t…
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network
Mary Gooneratne, Khe Chai Sim, Petr Zadrazil +3
Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models. However, one of the major obstacles to achieving this goa…
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
Khe Chai Sim, Françoise Beaufays, Arnaud Benard +9
We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ i…
An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models
Khe Chai Sim, Petr Zadrazil, Françoise Beaufays
Speaker-independent speech recognition systems trained with data from many users are generally robust against speaker variability and work well for a large population of speakers.…