Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning strategy for small-footprint spoken keyword spotting (PCL-KWS). Specifically, the proposed PCL-KWS framework introduces a network instantiator to generate the task-specific sub-networks for remembering previously learned keywords. As a result, the PCL-KWS approach incrementally learns new keywords without forgetting prior knowledge. Besides, the keyword-aware network scaling mechanism of PCL-KWS constrains the growth of model parameters while achieving high performance. Experimental results show that after learning five new tasks sequentially, our proposed PCL-KWS approach archives the new state-of-the-art performance of 92.8% average accuracy for all the tasks on Google Speech Command dataset compared with other baselines.

翻译：在部署后更新关键词识别模型( KWS) 时, 灾难性的遗忘是一个棘手的挑战。 为了应对这些挑战, 我们为小型脚印口语关键字识别模型( PCL- KWS) 提出了一个渐进式持续学习战略 。 具体地说, 拟议的PCL- KWS 框架引入了一个网络即时器, 以生成任务特定的子网络, 用于记住以前学过的关键字。 结果, PCL- KWS 方法在不忘记先前知识的情况下逐步学习新的关键字。 此外, PCL- KWS 关键字识别网络缩放机制限制了模型参数的增长, 同时又实现了高性能。 实验结果显示, 在连续学习五项新任务后, 我们提议的PCL- KWS 方法将谷歌语音命令数据集上所有任务的平均精度( 相对于其他基线) 的92.8 。