"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services
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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
- 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 151
- 2024Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gapscited by 24
- 2024Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generatorscited by 18
- 2024People are poorly equipped to detect AI-powered voice clonescited by 12
- 2023Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognitioncited by 5
- 2023No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulationcited by 4
Built on by
Citing papers per year: 2025: 1; 2026: 8.
9 papers.
- 2026Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarkingcited by 2
- 2025CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generationcited by 0
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversitycited by 0
- 2026Acoustic and perceptual differences between standard and accented speech and their voice clonescited by 0
- 2026"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Biascited by 0
- 2026Voice "Cloning" is Style Transfercited by 0
- 2026Sexualised synthetic personas encode and amplify gendered power asymmetries through voicecited by 0
- 2026AI systems and the reproduction of (standard) language ideologies in World Englishescited by 0
- 2026A Cross Community Agenda for Speech AIcited by 0
- 2026AI systems and the reproduction of (standard) language ideologies in World Englishes30 Jul 2026
- 2026A Cross Community Agenda for Speech AI22 Jul 2026
- 2026Sexualised synthetic personas encode and amplify gendered power asymmetries through voice19 Jun 2026
- 2026Voice "Cloning" is Style Transfer15 May 2026
- 2026"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias22 Apr 2026
- 2026Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking15 Apr 2026
- 2026Acoustic and perceptual differences between standard and accented speech and their voice clones2 Apr 2026
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity26 Jan 2026
- 2025CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation14 Nov 2025