Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required for edge devices due to their limited memory. To alleviate such an issue, we propose a novel diversity-aware incremental learning method named Rainbow Keywords (RK). Specifically, the proposed RK approach introduces a diversity-aware sampler to select a diverse set from historical and incoming keywords by calculating classification uncertainty. As a result, the RK approach can incrementally learn new tasks without forgetting prior knowledge. Besides, the RK approach also proposes data augmentation and knowledge distillation loss function for efficient memory management on the edge device. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline on Google Speech Command dataset with less required memory. The scripts are available on GitHub.

该研究提出了一种名为Rainbow Keywords的增量学习方法，其中使用一个多样性感知的采样器从历史和新的关键词中选择出一个多样性集合，从而在边缘设备上实现新任务的增量学习，并通过数据增强和知识蒸馏技术有效地管理内存。实验结果表明，该方法能在需要更少内存的情况下，在Google语音命令数据集上实现平均精度方面的4.2％绝对改进。

Rainbow Keywords: 在线口语关键词检测的高效增量学习