用KAN提升语音关键词识别性能，效果显著
Effective Integration of KAN for Keyword Spotting
- 将KAN融入1D CNN架构，优化特征提取
- 在低维空间有效建模高层特征，提升识别准确率
- 为语音及其他模态研究提供新思路
关键词识别（KWS）是具备语音助手功能智能设备的重要语音处理组件。本文探讨了柯尔莫哥洛夫-阿诺德网络（KAN）是否可用于提升KWS性能。我们研究了多种将KAN集成到基于一维卷积神经网络（1D CNN）的模型架构中的方法。结果表明，当适当地集成时，KAN能在低维空间中有效建模高层特征，从而提升KWS性能。研究为理解KAN在语音处理任务中的应用提供了洞见，并为未来其他模态的研究提供了参考。
原文摘要 · Abstract (English)
Keyword spotting (KWS) is an important speech processing component for smart devices with voice assistance capability. In this paper, we investigate if Kolmogorov-Arnold Networks (KAN) can be used to enhance the performance of KWS. We explore various approaches to integrate KAN for a model architecture based on 1D Convolutional Neural Networks (CNN). We find that KAN is effective at modeling high-level features in lower-dimensional spaces, resulting in improved KWS performance when integrated appropriately. The findings shed light on understanding KAN for speech processing tasks and on other modalities for future researchers.
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