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Automatic speech recognition (ASR) models are typically trained on largedatasets of transcribed speech. As language evolves and new terms come intouse, these models can become outdated and stale. In the context of modelstrained on the server but deployed on edge devices, errors may result from themismatch between server training data and actual on-device usage. In this work,we seek to continually learn from on-device user corrections through FederatedLearning (FL) to address this issue. We explore techniques to target freshterms that the model has not previously encountered, learn long-tail words, andmitigate catastrophic forgetting. In experimental evaluations, we find that theproposed techniques improve model recognition of fresh terms, while preservingquality on the overall language distribution.

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