The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning

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

Federated Pruning: Improving Neural Network Efficiency with Federated Learning

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

Online Model Compression for Federated Learning with Large Models

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.

Federated Learning Model Compression +3

Partial Variable Training for Efficient On-Device Federated Learning

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.

Federated Learning speech-recognition +1

Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training

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

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

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

Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework

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.

Federated Learning speech-recognition +1

Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network

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

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

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%.

speech-recognition Speech Recognition