发表机构
University of Calgary; Meta FAIR（卡尔加里大学; Meta FAIR）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文审计语音数据集中的酷儿代表性，发现其占比极低，并提出分类法以阐明收集边缘化社群语音数据时的实践张力。
AI 中文摘要
在本文中，我们审视了语音数据集中对LGBTQIA+（即酷儿）群体的包容性，并提供了一份关于张力的分类法，以更好地理解为何当前语音技术数据集中缺乏此类声音。通过对六个不同语音数据集的审计，我们发现可测量的酷儿代表性较低（占说话者的0-1.4%），不足以进行稳健的差异测量。我们以该社群为案例研究，探讨从边缘化社群收集语音数据所面临的挑战与张力。为作比较，我们还审计了语音科学领域中另外两个由酷儿社群创建、为其服务并与其合作的数据集。我们注意到，人工智能和语音技术研究中语音数据集收集的许多惯例，可能与在边缘化社群中强调的参与式方法所重视的价值观相冲突，并提供了描述这些张力的分类法。
英文摘要
In this paper, we examine speech datasets for their inclusion of LGBTQIA+, or queer, voices and provide a taxonomy of tensions to better understand why there is a lack of such voices in current speech technology datasets. Through an audit of six diverse speech datasets, we find that measurable queer representation is low (0-1.4% of speakers) - insufficient for robust disparity measurement. We take this community as a case study to consider what challenges and tensions are associated with collecting speech data from marginalized communities. For comparison, we audit an additional two datasets from the speech sciences that were created by, for, and with the queer community. We note that many customs in speech dataset collection efforts in AI and speech technology research may conflict with values emphasized in participatory approaches with marginalized communities, and provide a taxonomy describing these tensions.
CommentsAccepted at Interspeech 2026
