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

arXiv: https://arxiv.org/abs/2504.09346 · HTML: https://mlsanity.com/paper/2504.09346/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 88 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps](https://mlsanity.com/paper/2402.17954/) (2024)
- [Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators](https://mlsanity.com/paper/2402.01708/) (2024)
- [People are poorly equipped to detect AI-powered voice clones](https://mlsanity.com/paper/2410.03791/) (2024)
- [Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognition](https://mlsanity.com/paper/2302.12326/) (2023)
- [No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation](https://mlsanity.com/paper/2310.06590/) (2023)

## Built on by (most cited first, 9 of 9)

- [Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking](https://mlsanity.com/paper/2604.13776/) (2026)
- [CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation](https://mlsanity.com/paper/2511.11104/) (2025)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [Acoustic and perceptual differences between standard and accented speech and their voice clones](https://mlsanity.com/paper/2604.01562/) (2026)
- ["This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias](https://mlsanity.com/paper/2604.21148/) (2026)
- [Voice "Cloning" is Style Transfer](https://mlsanity.com/paper/2605.16578/) (2026)
- [Sexualised synthetic personas encode and amplify gendered power asymmetries through voice](https://mlsanity.com/paper/2606.21366/) (2026)
- [AI systems and the reproduction of (standard) language ideologies in World Englishes](https://mlsanity.com/paper/2607.28528/) (2026)
- [A Cross Community Agenda for Speech AI](https://mlsanity.com/paper/2609.13168/) (2026)

## 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.