Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting 14 the edge computing-associated costs. We show that accuracy improvements by up to 18% can be obtained by specialising on difficult, previously unseen noise types, on embedded devices with a power budget in the Watt range, with a storage requirement of 1.1GB. We also demonstrate an accuracy improvement of 1.43% on an ultra-low power platform consuming few-10 mW, requiring only 1.47 MB of memory kw the adaptation stage, at a one-time energy cost of 5.81J.
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published
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Book title
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
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Pages / Article No.
82 - 85
Publisher
IEEE
Event
IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS 2022)
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Methods
Software
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03996 - Benini, Luca / Benini, Luca