1.1k citations · 2.8k across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition
Tsendsuren Munkhdalai, Khe Chai Sim, Angad Chandorkar +4
Fast contextual adaptation has shown to be effective in improving Automatic Speech Recognition (ASR) of rare words and when combined with an on-device personalized training, it can…
Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning
Dongseong Hwang, Ananya Misra, Zhouyuan Huo +7
Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach mostly focus on i…
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.…