@inproceedings{he-etal-2026-audioprivacy,
    title = "{A}udio{P}rivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes",
    author = "He, Jiabei  and
      Zhang, Yanzhe  and
      Zhou, Jiaming  and
      Wang, Hui  and
      Sun, Haoqin  and
      Qin, Yong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.283/",
    pages = "5735--5748",
    ISBN = "979-8-89176-395-1",
    abstract = "Speech signals convey abundant speaker-related metadata, yet current privacy research predominantly focuses on identity-centric voiceprint protection, leaving sensitive Speaker Attribute Privacy (SAP) largely underexplored. This paper introduces AudioPrivacy, a large-scale Chinese dataset designed to systematically evaluate SAP leakage in realistic, everyday scenarios. Comprising 227.3 hours of audio from 1,000 speakers, it uniquely encompasses four parallel modalities: speech, singing, paralinguistic expressions, and non-vocal acoustic signals (e.g., footsteps). Annotated with 11 diverse attributes, including fine-grained physiological traits often overlooked in traditional corpora, AudioPrivacy enables a granular analysis of acoustic privacy risks. Our evaluations reveal significant leakage across multiple attributes, even when inferred from non-vocal signals. Furthermore, we demonstrate that state-of-the-art Multimodal Large Language Models (MM LLMs) can precisely profile speakers and exacerbate these risks, underscores the urgent need to rethink privacy-preserving mechanisms in the era of powerful audio foundation models."
}