mlsanityData to 7 Oct 2026

Just Because We Camp, Doesn't Mean We Should: The Ethics of Modelling Queer Voices

arXiv 2406.07504 ↗First posted 11 Jun 2024cs.CLcited by 3

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Abstract (arXiv)

Modern voice cloning models claim to be able to capture a diverse range of voices. We test the ability of a typical pipeline to capture the style known colloquially as "gay voice" and notice a homogenisation effect: synthesised speech is rated as sounding significantly "less gay" (by LGBTQ+ participants) than its corresponding ground-truth for speakers with "gay voice", but ratings actually increase for control speakers. Loss of "gay voice" has implications for accessibility. We also find that for speakers with "gay voice", loss of "gay voice" corresponds to lower similarity ratings. However, we caution that improving the ability of such models to synthesise ``gay voice'' comes with a great number of risks. We use this pipeline as a starting point for a discussion on the ethics of modelling queer voices more broadly. Collecting "clean" queer data has safety and fairness ramifications, and the resulting technology may cause harms from mockery to death.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

8 of 32 references.

References · 8

  1. 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 737
  2. 2018TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationcited by 224
  3. 2020Attention is All You Need in Speech Separationcited by 184
  4. 2021The Values Encoded in Machine Learning Researchcited by 172
  5. 2022ICASSP 2022 Deep Noise Suppression Challengecited by 57
  6. 2023Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Modelscited by 26
  7. 2021Ethics Sheets for AI Taskscited by 18
  8. 2024Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.