2024 INTERSPEECH INTERSPEECH 2024

How Private is Low-Frequency Speech Audio in the Wild? An Analysis of Verbal Intelligibility by Humans and Machines

Abstract

Low-frequency audio has been proposed as a promising privacy-preserving modality to study social dynamics in real-world settings. To this end, researchers have developed wearable devices that can record audio at frequencies as low as 1250 Hz to mitigate the automatic extraction of the verbal content of speech that may contain private details. This paper investigates the validity of this hypothesis, examining the degree to which low-frequency speech ensures verbal privacy. It includes simulating a potential privacy attack in various noise environments. Further, it explores the trade-off between the performance of voice activity detection, which is fundamental for understanding social behavior, and privacy-preservation. The evaluation incorporates subjective human intelligibility and automatic speech recognition performance, comprehensively analyzing the delicate balance between effective social behavior analysis and preserving verbal privacy.

โ“ The Questioner
๐ŸŒ‰ Interdisciplinary Bridge โ€” Artificial Intelligence and Machine Learning and Speech & Audio
๐Ÿงญ Keyword Pioneer โ€” low-frequency speech
๐Ÿ Cross-Pollinator โ€” Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Security & Privacy, Speech & Audio