@inproceedings{e529a78bc9354bfa99a04fa81fe32d7d,
title = "Jawthenticate: Microphone-free Speech-based Authentication using Jaw Motion and Facial Vibrations",
abstract = "In this paper, we present Jawthenticate, an earable system that authenticates a user using audible or inaudible speech without using a microphone. This system can overcome the shortcomings of traditional voice-based authentication systems like unreliability in noisy conditions and spoofing using microphone-based replay attacks. Jawthenticate derives distinctive speech-related features from the jaw motion and associated facial vibrations. This combination of features makes Jawthenticate resilient to vocal imitations as well as camera-based spoofing. We use these features to train a two-class SVM classifier for each user. Our system is invariant to the content and language of speech. In a study conducted with 41 subjects, who speak different native languages, Jawthenticate achieves a Balanced Accuracy (BAC) of 97.07\%, True Positive Rate (TPR) of 97.75\%, and True Negative Rate (TNR) of 96.4\% with just 3 seconds of speech data.",
keywords = "IMU sensing, biometrics, signal processing, speech authentication",
author = "Tanmay Srivastava and Shijia Pan and Phuc Nguyen and Shubham Jain",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright is held by the owner/author(s). Publication rights licensed to ACM.; 21st ACM Conference on Embedded Networked Sensors Systems, SenSys 2023 ; Conference date: 13-11-2023 Through 15-11-2023",
year = "2024",
month = apr,
day = "26",
doi = "10.1145/3625687.3625813",
language = "English",
series = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "209--222",
booktitle = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
}