6533b7d0fe1ef96bd125b9dc

RESEARCH PRODUCT

Low-Power Audio Keyword Spotting using Tsetlin Machines

Tousif RahmanJie LeiAlex YakovlevFahim KawsarAkhil MathurOle-christoffer GranmoAdrian WheeldonRishad Shafik

subject

FOS: Computer and information sciencesspeech commandSound (cs.SD)Computer scienceSpeech recognition02 engineering and technologykeyword spottingMachine learningcomputer.software_genreComputer Science - SoundReduction (complexity)Audio and Speech Processing (eess.AS)020204 information systemsFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringArtificial neural networkLearning automatabusiness.industrylearning automatalcsh:Applications of electric power020206 networking & telecommunicationslcsh:TK4001-4102Pipeline (software)Power (physics)machine learningTsetlin MachineMFCCKeyword spottingelectrical_electronic_engineeringScalabilityMemory footprintpervasive AI020201 artificial intelligence & image processingMel-frequency cepstrumArtificial intelligencebusinesscomputerartificial neural networkEfficient energy useElectrical Engineering and Systems Science - Audio and Speech Processing

description

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.

yearjournalcountryeditionlanguage
2021-01-29
http://arxiv.org/abs/2101.11336