发表机构
National Institute of Informatics; University of Science and Technology of China（国立情报学研究所; 中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
VoxTubeS提出可再分发的说话人匿名合成语音语料库家族，基于VoxTube构建七个变体，通过多维度评估揭示隐私、实用性与多样性间的权衡，无单一最优方法。
AI 中文摘要
大型语音语料库支持研究，但录音可能暴露说话人身份，因为语音仍是可识别的生物特征。同时，源自媒体的语音数据难以可靠地再分发。我们提出VoxTubeS，一个面向再分发的说话人匿名合成语音语料库家族，包含三个方法族和七个变体，源自VoxTube语料库（在CC BY-NC-SA 4.0许可下分发），使用来自1,511位说话人的129万条质量过滤的英语话语。合成方法涵盖语音转换、潜空间匿名化和可控文本到语音。我们使用话语级不可链接性、对话级可链接性和单独识别、下游说话人验证、语言一致性、说话人多样性以及性别和口音的公平性指标来评估VoxTubeS。我们的综合分析揭示了复杂的权衡：更强的身份抑制通常降低可链接性，但牺牲实用性和群体多样性，而说话人一致性训练同时提高话语级和对话级隐私，同时保持相当的实用性和更广泛的说话人空间。公平性独立于整体性能而变化。没有方法占优；因此，VoxTubeS将语料库构建视为在源许可下平衡隐私、实用性、多样性、公平性和负责任再分发的操作点之间的选择。
英文摘要
Large speech corpora support research, but recordings can expose speaker identity because voice remains a recognizable biometric. Meanwhile, speech data derived from media can be difficult to redistribute reliably. We present \emph{VoxTubeS}, a family of speaker-anonymized synthetic speech corpora designed for redistribution, comprising three method families and seven variants derived from the VoxTube corpus, which is distributed under CC BY-NC-SA 4.0, using 1.29M quality-filtered English utterances from 1,511 speakers. The synthesis methods span voice conversion, latent-space anonymization, and controllable text-to-speech. We evaluate VoxTubeS using utterance-level unlinkability, conversation-level linkability and singling-out, downstream speaker verification, linguistic consistency, speaker diversity, and fairness metrics for gender and accents. Our comprehensive analysis exposes a complex trade-off: stronger identity suppression often reduces linkability but sacrifices utility and population diversity, whereas speaker consistency training improves both utterance- and conversation-level privacy while retaining comparable utility and a broader speaker space. Fairness varies independently of aggregate performance. No method dominates; VoxTubeS therefore treats corpus construction as a choice among operating points that balances privacy, utility, diversity, fairness, and responsible redistribution under the source license.
