Automatic speech recognition models are often adapted to improve their
accuracy in a new domain. A potential drawback of model adaptation to new
domains is catastrophic forgetting, where the Word Error Rate on the original
domain is significantly degraded. This paper addresses the situation when we
want to simultaneously adapt automatic speech recognition models to a new
domain and limit the degradation of accuracy on the original domain without
access to the original training dataset. We propose several techniques such as
a limited training strategy and regularized adapter modules for the Transducer
encoder, prediction, and joiner network. We apply these methods to the Google
Speech Commands and to the UK and Ireland English Dialect speech data set and
obtain strong results on the new target domain while limiting the degradation
on the original domain.

本文探讨了如何同时适应新领域和限制模型在原领域的准确性降低的问题，提出了几种技术，如限制培训策略和规范化适配器模块，应用于 Google Speech Commands 和英国爱尔兰英语口音数据集，并在新的目标域上取得了良好的结果并限制了原始领域上的恶化。