most citedPersonalization of End-to-end Speech Recognition On Mobile Devices For Named Entities

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2021

Enabling On-Device Training of Speech Recognition Models with Federated Dropout

Dhruv Guliani, Lillian Zhou, Changwan Ryu +5

Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents a challenge pertaini…

eess.AS2019★ 1 cited

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…