no code implementations • 29 Sep 2023 • Lillian Zhou, Yuxin Ding, Mingqing Chen, Harry Zhang, Rohit Prabhavalkar, Dhruv Guliani, Giovanni Motta, Rajiv Mathews
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 14 Sep 2022 • Rongmei Lin, Yonghui Xiao, Tien-Ju Yang, Ding Zhao, Li Xiong, Giovanni Motta, Françoise Beaufays
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 6 May 2022 • Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv Mathews, Mingqing Chen
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost.
no code implementations • 11 Oct 2021 • Tien-Ju Yang, Dhruv Guliani, Françoise Beaufays, Giovanni Motta
This paper aims to address the major challenges of Federated Learning (FL) on edge devices: limited memory and expensive communication.
no code implementations • 8 Oct 2021 • Lillian Zhou, Dhruv Guliani, Andreas Kabel, Giovanni Motta, Françoise Beaufays
Transformer-based architectures have been the subject of research aimed at understanding their overparameterization and the non-uniform importance of their layers.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 7 Oct 2021 • Dhruv Guliani, Lillian Zhou, Changwan Ryu, Tien-Ju Yang, Harry Zhang, Yonghui Xiao, Francoise Beaufays, Giovanni Motta
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 29 Oct 2020 • Dhruv Guliani, Francoise Beaufays, Giovanni Motta
We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models.
no code implementations • 24 Jan 2020 • Mary Gooneratne, Khe Chai Sim, Petr Zadrazil, Andreas Kabel, Françoise Beaufays, Giovanni Motta
Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 14 Dec 2019 • Khe Chai Sim, Françoise Beaufays, Arnaud Benard, Dhruv Guliani, Andreas Kabel, Nikhil Khare, Tamar Lucassen, Petr Zadrazil, Harry Zhang, Leif Johnson, Giovanni Motta, Lillian Zhou
With speech input, if the user corrects only the names, the name recall rate improves to 64. 4%.