Effective Integration of KAN for Keyword Spotting

Autor:	Xu, Anfeng, Zhang, Biqiao, Kong, Shuyu, Huang, Yiteng, Yang, Zhaojun, Srivastava, Sangeeta, Sun, Ming
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	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. Comment: Under review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.08605 Zobrazit plný text záznamu View this record from Arxiv