论文标题
彩虹关键字：在线口语关键字发现的有效增量学习
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
论文作者
论文摘要
部署后更新关键字发现（KWS）模型时，灾难性遗忘是一个棘手的挑战。如果KWS模型由于内存有限而进一步需要KWS模型，则此问题将更具挑战性。为了减轻这一问题，我们提出了一种名为Rainbow关键词（RK）的新型多样性吸引的增量学习方法。具体而言，拟议的RK方法引入了一种多样性感知的采样器，以通过计算分类不确定性来从历史和传入的关键字中选择多种设置。结果，RK方法可以逐步学习新任务而无需忘记先验知识。此外，RK方法还提出了数据的增强和知识蒸馏损失功能，以在边缘设备上有效内存管理。实验结果表明，所提出的RK方法在与Google Speech命令数据集中最佳基线的平均准确性相比，绝对准确性可实现4.2％的绝对改善，并且所需的内存较少。这些脚本可在GitHub上找到。
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.
