Effective Integration of KAN for Keyword Spotting

Abstract

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.

Accepted to ICASSP 2025

References in corpus (1)

• KAN or MLP: A Fairer Comparison

Cited by in corpus (1)

• Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks