Towards speaker identification on resource-constrained embedded devices
Loading...
Files
Published version
Date
2023-11-12
Authors
Gallacher, Markus
Boano, Carlo Alberto
Sankar, M. S. Arun
Roedig, Utz
Lunardi, Willian T.
Baddeley, Michael
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Published Version
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.
Description
Keywords
Machine learning , Speaker identification , Embedded systems , Deep learning (DL)
Citation
Gallacher, 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).
Link to publisher’s version
Collections
Copyright
© 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).