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Found 6 papers, 0 papers with code
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Partial Variable Training for Efficient On-Device Federated Learning
This paper aims to address the major challenges of Federated Learning (FL) on edge devices: limited memory and expensive communication.
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training
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
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
We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models.
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
With speech input, if the user corrects only the names, the name recall rate improves to 64. 4%.
