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Poster Abstract: Towards Speaker Identification on Resource-Constrained Embedded Devices

  • Markus Gallacher
  • , Carlo Alberto Boano
  • , M. S.Arun Sankar
  • , Utz Roedig
  • , Willian T. Lunardi
  • , Michael Baddeley
  • Graz University of Technology
  • University College Cork
  • Technology Innovation Institute

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Voice is a convenient and popular way to interact with our digital world. Besides translating speech to text, it is also possible to identify speakers based on their voice profile. To date, speaker identification has predominantly been limited to high-performance computational platforms owing to the intricate nature of the underlying algorithms. In this work, we demonstrate that it is possible to reduce model complexity by the required factor of ∼10, such that speaker identification can be made feasible for embedded devices with limited resources. We further describe and discuss novel use cases, such as voice-based presence detection and authentication, that become feasible on these class of devices.

Original languageEnglish
Title of host publicationSenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems
PublisherAssociation for Computing Machinery, Inc
Pages518-519
Number of pages2
ISBN (Electronic)9798400704147
DOIs
Publication statusPublished - 12 Nov 2023
Event21st ACM Conference on Embedded Networked Sensors Systems, SenSys 2023 - Istanbul, Turkey
Duration: 13 Nov 202315 Nov 2023

Publication series

NameSenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems

Conference

Conference21st ACM Conference on Embedded Networked Sensors Systems, SenSys 2023
Country/TerritoryTurkey
CityIstanbul
Period13/11/2315/11/23

Keywords

  • embedded systems
  • machine learning
  • speaker identification

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