mlsanityData to 7 Oct 2026

"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services

arXiv 2504.09346 ↗First posted 12 Apr 2025cs.HC cs.AI cs.CYcited by 9

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Abstract (arXiv)

Recent advances in artificial intelligence (AI) speech generation and voice cloning technologies have produced naturalistic speech and accurate voice replication, yet their influence on sociotechnical systems across diverse accents and linguistic traits is not fully understood. This study evaluates two synthetic AI voice services (Speechify and ElevenLabs) through a mixed methods approach using surveys and interviews to assess technical performance and uncover how users' lived experiences influence their perceptions of accent variations in these speech technologies. Our findings reveal technical performance disparities across five regional, English-language accents and demonstrate how current speech generation technologies may inadvertently reinforce linguistic privilege and accent-based discrimination, potentially creating new forms of digital exclusion. Overall, our study highlights the need for inclusive design and regulation by providing actionable insights for developers, policymakers, and organizations to ensure equitable and socially responsible AI speech technologies.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 6 of its 88 references that are in the map. First posted on arXiv 12 Apr 2025.

Builds on

6 of 88 references.

References · 6

  1. 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 151
  2. 2024Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gapscited by 24
  3. 2024Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generatorscited by 18
  4. 2024People are poorly equipped to detect AI-powered voice clonescited by 12
  5. 2023Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognitioncited by 5
  6. 2023No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 8.

9 papers.