  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.
