Safeguarding Voice Privacy: Harnessing NearUltrasonic Interference to Protect Against Unauthorized Audio Recording

Forrest McKee and David Noever, PeopleTec, USA

Abstract

The widespread adoption of voiceactivated systems has modified routine humanmachine interaction but has also introduced new vulnerabilities. This paper investigates the susceptibility of automatic speech recognition (ASR) algorithms in these systems to interference from nearultrasonic noise. Building upon prior research that demonstrated the ability of nearultrasonic frequencies (16 kHz 22 kHz) to exploit the inherent properties of microelectromechanical systems (MEMS) microphones, our study explores alternative privacy enforcement means using this interference phenomenon. We expose a critical vulnerability in the most common microphones used in modern voiceactivated devices, which inadvertently demodulate nearultrasonic frequencies into the audible spectrum, disrupting the ASR process. Through a systematic analysis of the impact of nearultrasonic noise on various ASR systems, we demonstrate that this vulnerability is consistent across different devices and under varying conditions, such as broadcast distance and specific phoneme structures. Our findings highlight the need to develop robust countermeasures to protect voiceactivated systems from malicious exploitation of this vulnerability. Furthermore, we explore the potential applications of this phenomenon in enhancing privacy by disrupting unauthorized audio recording or eavesdropping. This research underscores the importance of a comprehensive approach to securing voiceactivated systems, combining technological innovation, responsible development practices, and informed policy decisions to ensure the privacy and security of users in an increasingly connected world

Keywords

cybersecurity, voiceactivated systems, automatic speech recognition, nearultrasonic frequencies, MEMS microphones, privacy, acoustic interference, internet of things, digital signal processing, audio forensics

Full Text : https://aircconline.com/csit/papers/v...
Abstract URL : https://aircconline.com/csit/abstract...
Volume URL : https://airccse.org/csit/V14N11.html

#cybersecurity #privacy #internetofthings #speechrecognition #digitalsignalprocessing