Low-Power Audio Keyword Spotting Using Tsetlin Machines

• Published in: Journal of low power electronics and applications Vol. 11; no. 2; p. 18
• Main Authors: Lei, Jie, Rahman, Tousif, Shafik, Rishad, Wheeldon, Adrian, Yakovlev, Alex, Granmo, Ole-Christoffer, Kawsar, Fahim, Mathur, Akhil
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: Accuracy; Algorithms; Architecture; Artificial intelligence; Boolean; Complexity; Design; Embedded systems; Energy conversion efficiency; Energy efficiency; Feedback; Internet of Things; keyword spotting; Keywords; learning automata; Machine learning; MFCC; Neural networks; pervasive AI; speech command; Tsetlin Machine
• ISSN: 2079-9268; 2079-9268
• DOI: 10.3390/jlpea11020018

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.