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 As language evolves and new terms come into use, these models can become outdated and stale.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.

翻译：自动语音识别（ASR）模型通常在大规模转录语音数据集上训练。随着语言演进及新术语的出现，这些模型可能变得过时和陈旧。在服务器端训练但部署于边缘设备的模型场景中，服务器训练数据与实际设备端使用之间的不匹配可能导致错误。本文致力于通过联邦学习（FL）持续从设备端的用户纠错中学习，以解决该问题。我们探索了针对模型未见过的新术语、学习长尾词汇以及缓解灾难性遗忘的技术。实验评估表明，所提技术在提升模型对新术语识别能力的同时，保持了整体语言分布的识别质量。