@inproceedings{0af76184aff74997b5a643c85b38dce0,
title = "Privacy Preserving Radio Frequency Speech Sensing with Deep Learning Towards Improved Hearing Aids",
abstract = "Hearing loss is a profound public health issue typically addressed with microphone sensing hearing aids calibrated to compensate for an individual's hearing loss pattern. However, microphones often generate low sound quality in noisy environments resulting in low device adoption. This paper explores use of noise resistant Radio Frequency (RF) radar speech sensing micro-Doppler (μD) shifts generated from a speaker's vocal tract with Deep Learning (DL) speech classification for audio replay. It extends earlier work using a vowels dataset to whole sentences to train and test seven DL model types that gave encouraging accuracy results ranging from 75\% to 91\%. Model training time typically took 4 minutes and no more than 20 epochs to converge with loss rates suggesting potential viability of RF μD sensing for more complex vocabularies.",
author = "Michaela Reay and Hira Hameed and Imran, \{Muhammad Ali\} and Abbasi, \{Qammer H.\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 ; Conference date: 14-07-2024 Through 19-07-2024",
year = "2024",
doi = "10.1109/AP-S/INC-USNC-URSI52054.2024.10686586",
language = "English",
series = "IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2387--2388",
booktitle = "2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings",
address = "United States",
}