How Private is Low-Frequency Speech Audio in the Wild? An Analysis of Verbal Intelligibility by Humans and Machines
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913435211530240 |
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| author | Liu, Ailin Vunderink, Pepijn Quiros, Jose Vargas Raman, Chirag Hung, Hayley |
| author_facet | Liu, Ailin Vunderink, Pepijn Quiros, Jose Vargas Raman, Chirag Hung, Hayley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Private is Low-Frequency Speech Audio in the Wild? An Analysis of Verbal Intelligibility by Humans and Machines Liu, Ailin Vunderink, Pepijn Quiros, Jose Vargas Raman, Chirag Hung, Hayley Sound Human-Computer Interaction Audio and Speech Processing 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. |
| title | How Private is Low-Frequency Speech Audio in the Wild? An Analysis of Verbal Intelligibility by Humans and Machines |
| topic | Sound Human-Computer Interaction Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.13266 |