Queer inclusion in speech datasets: An audit and taxonomy of practical tensions

2026-09-21

AI Summary

本文通过审计六个语音数据集，探讨了LGBTQIA+群体在其中的代表性不足问题，并提出了一种分类法来理解这种现象背后的原因和挑战。

Abstract

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.

Problem

• Queer inclusion
• speech datasets
• marginalized communities

Innovation

• Queer Representation
• Speech Datasets
• Taxonomy of Tensions
• Marginalized Communities
• Participatory Approaches