@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T AudioPrivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes
%A He, Jiabei
%A Zhang, Yanzhe
%A Zhou, Jiaming
%A Wang, Hui
%A Sun, Haoqin
%A Qin, Yong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F he-etal-2026-audioprivacy
%X 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.
%U https://aclanthology.org/2026.findings-acl.283/
%P 5735-5748
Markdown (Informal)
[AudioPrivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes](https://aclanthology.org/2026.findings-acl.283/) (He et al., Findings 2026)
ACL