用二值神经网络实现低资源设备上关键词的持续学习。
Efficient Continual Learning in Keyword Spotting using Binary Neural Networks
- 基于二值神经网络构建持续学习框架，降低计算与内存开销。
- 新增1个关键词准确率超95%，4个新类仍保持86%准确率。
- 适合在嵌入式设备上部署，对训练数据量不敏感。
关键词识别（KWS）是智能设备实现交互的核心功能。但在资源受限设备上，现有模型通常为静态，难以适应新增关键词等新场景。为此，本文提出一种基于二值神经网络（BNNs）的持续学习（CL）方法，兼顾低计算与内存需求，并支持随时间无缝集成新关键词。在16类任务中评估了七种CL技术，结果显示：仅新增一个关键词时准确率超过95%，新增四个类别时准确率仍达86%。研究还发现，基于批次的算法对持续学习阶段的数据量更敏感，而不同方法间的计算复杂度差异不显著。这些结果表明，该方法在资源受限设备上具备高效、稳定地持续集成新关键词的潜力。
原文摘要 · Abstract (English)
Keyword spotting (KWS) is an essential function that enables interaction with ubiquitous smart devices. However, in resource-limited devices, KWS models are often static and can thus not adapt to new scenarios, such as added keywords. To overcome this problem, we propose a Continual Learning (CL) approach for KWS built on Binary Neural Networks (BNNs). The framework leverages the reduced computation and memory requirements of BNNs while incorporating techniques that enable the seamless integration of new keywords over time. This study evaluates seven CL techniques on a 16-class use case, reporting an accuracy exceeding 95% for a single additional keyword and up to 86% for four additional classes. Sensitivity to the amount of training samples in the CL phase, and differences in computational complexities are being evaluated. These evaluations demonstrate that batch-based algorithms are more sensitive to the CL dataset size, and that differences between the computational complexities are insignificant. These findings highlight the potential of developing an effective and computationally efficient technique for continuously integrating new keywords in KWS applications that is compatible with resource-constrained devices.
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