12 citations · 28 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
Katrin Tomanek, Françoise Beaufays, Julie Cattiau +2
While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordere…
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.…
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Bo Li, Tara N. Sainath, Khe Chai Sim +6
Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM),…