Markus Gallacher
Utz Roedig
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.
- A. Hajavi and A. Etemad. 2019. A Deep Neural Network for Short-Segment Speaker Recognition. In Proc. of Interspeech'19. Google Scholar
Cross Ref
- M. Jakubec et al. 2021. Speaker Recognition with ResNet and VGG Networks. In Proc. of RADIOELEKTRONIKA'19. Google ScholarCross Ref
- S. Koppula et al. 2018. Energy-Efficient Speaker Identification with Low-Precision Networks. In Proc. of ICASSP'18. Google ScholarDigital Library
- C. Nunes et al. 2020. AM-MobileNet1D: A Portable Model for Speaker Recognition. In Proc. of IJCNN'20. Google ScholarCross Ref
- S.S. Tirumala and S.R. Shahamiri. 2016. A Review on Deep Learning Approaches in Speaker Identification. In Proc. of ICSPS'16. Google ScholarDigital Library
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