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use, these models can become outdated and stale.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.

使用联邦学习的方法，从设备端用户的纠正中持续学习以解决自动语音识别模型在识别新时代术语方面的过时和陈旧问题。实验评估表明，该方法能够提高模型对新时代术语的识别能力，并保持整体语言分布的质量。