AnalyticKWS：面向小足迹关键词唤醒的无样本解析类增量学习
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
AI总结：
针对小足迹关键词唤醒的持续学习中旧样本存储带来的隐私与资源问题，提出无样本 AnalyticKWS，以闭式解析更新和单轮适应避免反向传播，并在多数据集上取得更优性能。
摘要：
关键词唤醒（KWS）为语音交互系统识别口语命令提供了关键机制，而用户需求经常变化，要求模型随时间持续学习新关键词。然而，一个主要问题是灾难性遗忘，即模型丧失识别早期关键词的能力。尽管多种持续学习方法已被证明有助于减少遗忘，但大多数现有方法依赖存储和重访旧数据来对抗灾难性遗忘。这些方法虽然有效，却面临两项实际挑战：1）保存用户数据带来的隐私风险；2）巨大的内存与时间消耗限制了其在小型设备上的部署。为解决这些问题，我们提出一种无样本的解析持续学习方法 AnalyticKWS，它无需重访早期数据即可更新模型参数。受高效学习原则启发，AnalyticKWS 为模型更新计算闭式解析解，并且对新到来的关键词只需一个 epoch 的适应。通过避免基于梯度的更新，AnalyticKWS 需要更少的计算资源，并且不存储旧数据。由于在增量学习过程中消除了反向传播需求，该模型保持轻量且高效。因此，AnalyticKWS 应对了前述挑战，并非常适合资源受限环境。在多种数据集和设置上的大量实验表明，AnalyticKWS 始终优于现有持续学习方法。
英文摘要：
Keyword spotting (KWS) offers a vital mechanism to identify spoken commands in voice-enabled systems, where user demands often shift, requiring models to learn new keywords continually over time. However, a major problem is catastrophic forgetting, where models lose their ability to recognize earlier keywords. Although several continual learning methods have proven their usefulness for reducing forgetting, most existing approaches depend on storing and revisiting old data to combat catastrophic forgetting. Though effective, these methods face two practical challenges: 1) privacy risks from keeping user data and 2) large memory and time consumption that limit deployment on small devices. To address these issues, we propose an exemplar-free Analytic Continual Learning (AnalyticKWS) method that updates model parameters without revisiting earlier data. Inspired by efficient learning principles, AnalyticKWS computes a closed-form analytical solution for model updates and requires only a single epoch of adaptation for incoming keywords. AnalyticKWS demands fewer computational resources by avoiding gradient-based updates and does not store old data. By eliminating the need for back-propagation during incremental learning, the model remains lightweight and efficient. As a result, AnalyticKWS meets the challenges mentioned earlier and suits resource-limited settings well. Extensive experiments on various datasets and settings show that AnalyticKWS consistently outperforms existing continual learning methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
