  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.
