Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Low-Power Audio Keyword Spotting using Tsetlin Machines

Jie Lei
,
Tousif Rahman
,
Rishad Shafik
* ORCID logo ,
Alex Yakovlev
,
Alex Yakovlev
,
Ole-Christoffer Granmo
,
Fahim Kawsar
,
and Mathur
Version 1 : Received: 28 January 2021 / Approved: 29 January 2021 / Online: 29 January 2021 (13:01:47 CET)

A peer-reviewed article of this Preprint also exists.

Lei, J.; Rahman, T.; Shafik, R.; Wheeldon, A.; Yakovlev, A.; Granmo, O.-C.; Kawsar, F.; Mathur, A. Low-Power Audio Keyword Spotting Using Tsetlin Machines. J. Low Power Electron. Appl. 2021, 11, 18. Lei, J.; Rahman, T.; Shafik, R.; Wheeldon, A.; Yakovlev, A.; Granmo, O.-C.; Kawsar, F.; Mathur, A. Low-Power Audio Keyword Spotting Using Tsetlin Machines. J. Low Power Electron. Appl. 2021, 11, 18.

Abstract

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

Keywords

Speech Command; MFCC; Tsetlin Machine; Learning Automata; Pervasive AI; Machine Learning; Artificial Neural Network; Keyword Spotting

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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