# Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity

arXiv: https://arxiv.org/abs/2601.18641 · HTML: https://mlsanity.com/paper/2601.18641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 127 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 30 of 31)

- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Selection Problems in the Presence of Implicit Bias](https://mlsanity.com/paper/1801.03533/) (2018)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review](https://mlsanity.com/paper/2211.09511/) (2022)
- [From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition](https://mlsanity.com/paper/2302.09044/) (2023)
- [Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation](https://mlsanity.com/paper/2401.00662/) (2024)
- [Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech](https://mlsanity.com/paper/2405.06150/) (2024)
- [FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications](https://mlsanity.com/paper/2407.18745/) (2024)
- [Measuring the Accuracy of Automatic Speech Recognition Solutions](https://mlsanity.com/paper/2408.16287/) (2024)
- [What Do We Mean by "Accessibility Research"? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019](https://mlsanity.com/paper/2101.04271/) (2021)
- [Analysis and Tuning of a Voice Assistant System for Dysfluent Speech](https://mlsanity.com/paper/2106.11759/) (2021)
- [A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures](https://mlsanity.com/paper/2303.00164/) (2023)
- [Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis](https://mlsanity.com/paper/2206.06149/) (2022)
- [Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition](https://mlsanity.com/paper/2310.18877/) (2023)
- ["It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services](https://mlsanity.com/paper/2504.09346/) (2025)
- [See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers](https://mlsanity.com/paper/2006.06328/) (2020)
- [Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR](https://mlsanity.com/paper/2202.12603/) (2022)
- [Deaf, Hard of Hearing, and Hearing Perspectives on using Automatic Speech Recognition in Conversation](https://mlsanity.com/paper/1909.01176/) (2019)
- [Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition](https://mlsanity.com/paper/2208.01157/) (2022)
- [Automatic Speech Recognition Biases in Newcastle English: an Error Analysis](https://mlsanity.com/paper/2506.16558/) (2025)
- [Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognition](https://mlsanity.com/paper/2302.12326/) (2023)
- [Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation](https://mlsanity.com/paper/2406.10177/) (2024)
- [Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens](https://mlsanity.com/paper/2508.07143/) (2025)
- [Different Types of Voice User Interface Failures May Cause Different Degrees of Frustration](https://mlsanity.com/paper/2002.03582/) (2020)
- [Fine-Tuning ASR for Stuttered Speech: Personalized vs. Generalized Approaches](https://mlsanity.com/paper/2506.00853/) (2025)
- [Will AI shape the way we speak? The emerging sociolinguistic influence of synthetic voices](https://mlsanity.com/paper/2504.10650/) (2025)
- [Pardon? An Overview of the Current State and Requirements of Voice User Interfaces for Blind and Visually Impaired Users](https://mlsanity.com/paper/2203.05848/) (2022)
- [Inclusivity of AI Speech in Healthcare: A Decade Look Back](https://mlsanity.com/paper/2505.10596/) (2025)

## Abstract (arXiv)

Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society. While fluency is often equated with credibility and competence, individuals with atypical speech patterns are routinely marginalized. Given the current state of the debate, this article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence. Automated speech recognition (ASR) systems and voice interfaces, trained predominantly on standardized speech, routinely fail to recognize or respond to diverse voices, compounding digital exclusion. As AI technologies increasingly mediate access to opportunity, the study calls for inclusive technological design, anti-bias training to minimize the impact of discriminatory algorithmic decisions, and enforceable policy reform that explicitly recognize speech diversity as a matter of equity, not merely accessibility. Drawing on interdisciplinary research, the article advocates for a cultural and institutional shift in how we value voice, urging co-created solutions that elevate the rights, representation, and realities of atypical speakers in the digital age. Ultimately, the article reframes speech inclusion as a matter of equity (not accommodation) and advocates for co-created AI systems that reflect the full spectrum of human voices.