Towards speaker identification on resource-constrained embedded devices

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dc.contributor.authorGallacher, Markusen
dc.contributor.authorBoano, Carlo Albertoen
dc.contributor.authorSankar, M. S. Arunen
dc.contributor.authorRoedig, Utzen
dc.contributor.authorLunardi, Willian T.en
dc.contributor.authorBaddeley, Michaelen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-11-13T12:55:58Z
dc.date.available2023-11-13T12:55:58Z
dc.date.issued2023-11-12en
dc.description.abstractVoice 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.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGallacher, M., Boano, C. A., Sankar, M. S. A., Roedig, U., Lunardi, W. and Baddeley, M. (2023) Poster Abstract: Towards Speaker Identification on Resource-Constrained Embedded Devices, 21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23), Istanbul, Turkiye, November 12-17. ACM, New York, NY, USA, (2 pp).en
dc.identifier.endpage2en
dc.identifier.isbn979-8-4007-0414-7/23/11
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15224
dc.language.isoenen
dc.publisherACMen
dc.relation.ispartof21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23),en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Frontiers for the Future::Award/19/FFP/6775/IE/Personal Voice Assistant Security and Privacy/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/13/RC/2077_P2/IE/CONNECT_Phase 2/en
dc.rights© 2023 Copyright held by the owner/author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).en
dc.subjectMachine learningen
dc.subjectSpeaker identificationen
dc.subjectEmbedded systemsen
dc.subjectDeep learning (DL)en
dc.titleTowards speaker identification on resource-constrained embedded devicesen
dc.typeConference itemen
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