The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
This addresses the issue of stale ASR models for users on edge devices, though it is incremental as it builds on existing FL methods.
The paper tackled the problem of outdated ASR models by using Federated Learning to learn from on-device user corrections, resulting in improved recognition of fresh terms while maintaining overall language quality.
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech. As language evolves and new terms come into use, these models can become outdated and stale. In the context of models trained on the server but deployed on edge devices, errors may result from the mismatch between server training data and actual on-device usage. In this work, we seek to continually learn from on-device user corrections through Federated Learning (FL) to address this issue. We explore techniques to target fresh terms that the model has not previously encountered, learn long-tail words, and mitigate catastrophic forgetting. In experimental evaluations, we find that the proposed techniques improve model recognition of fresh terms, while preserving quality on the overall language distribution.