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New Challenges for Content Privacy in Speech and Audio

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arxiv 2301.08925 v1 pith:X2XMTNOZ submitted 2023-01-21 eess.AS cs.CRcs.SD

classification eess.AScs.CRcs.SD
keywords speechprivacyaudiocontenttechnologieschallengeseveninformation
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Privacy in speech and audio has many facets. A particularly under-developed area of privacy in this domain involves consideration for information related to content and context. Speech content can include words and their meaning or even stylistic markers, pathological speech, intonation patterns, or emotion. More generally, audio captured in-the-wild may contain background speech or reveal contextual information such as markers of location, room characteristics, paralinguistic sounds, or other audible events. Audio recording devices and speech technologies are becoming increasingly commonplace in everyday life. At the same time, commercialised speech and audio technologies do not provide consumers with a range of privacy choices. Even where privacy is regulated or protected by law, technical solutions to privacy assurance and enforcement fall short. This position paper introduces three important and timely research challenges for content privacy in speech and audio. We highlight current gaps and opportunities, and identify focus areas, that could have significant implications for developing ethical and safer speech technologies.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FeatureSense: Protecting Speaker Attributes in Always-On Audio Sensing System

    cs.SD 2025-05 conditional novelty 6.0 of 10

    FeatureSense exposes hand-picked audio features instead of raw audio and introduces the SILI metric, claiming 60.6% lower speaker attribute leakage while keeping sound classification accuracy.

  2. Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks

    cs.CR 2025-01 reject novelty 4.0 of 10

    Pitch and tempo edits made with consumer audio apps frequently confuse emotion-recognition models, but the paper's evidence is too limited to support its strong privacy claim.

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