Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms

TL;DR

This paper reviews recent advancements in small-footprint keyword spotting, focusing on efficient models and algorithms suitable for low-power edge devices, and discusses future research directions in the field.

Contribution

It provides a comprehensive overview of seven categories of techniques for developing efficient SF-KWS systems, integrating insights from speech recognition and TinyML.

Findings

Identification of key techniques for model efficiency

Analysis of current challenges in edge deployment

Future research directions in SF-KWS

Abstract

Small-Footprint Keyword Spotting (SF-KWS) has gained popularity in today's landscape of smart voice-activated devices, smartphones, and Internet of Things (IoT) applications. This surge is attributed to the advancements in Deep Learning, enabling the identification of predefined words or keywords from a continuous stream of words. To implement the SF-KWS model on edge devices with low power and limited memory in real-world scenarios, a efficient Tiny Machine Learning (TinyML) framework is essential. In this study, we explore seven distinct categories of techniques namely, Model Architecture, Learning Techniques, Model Compression, Attention Awareness Architecture, Feature Optimization, Neural Network Search, and Hybrid Approaches, which are suitable for developing an SF-KWS system. This comprehensive overview will serve as a valuable resource for those looking to understand, utilize, or contribute to the field of SF-KWS. The analysis conducted in this work enables the identification of numerous potential research directions, encompassing insights from automatic speech recognition research and those specifically pertinent to the realm of spoken SF-KWS.