# Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review

arXiv: https://arxiv.org/abs/2211.09511 · HTML: https://mlsanity.com/paper/2211.09511/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 72 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [Learning Fast Adaptation on Cross-Accented Speech Recognition](https://mlsanity.com/paper/2003.01901/) (2020)
- [Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions](https://mlsanity.com/paper/2111.09983/) (2021)
- [Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance](https://mlsanity.com/paper/1908.08717/) (2019)
- [Toward Fairness in AI for People with Disabilities: A Research Roadmap](https://mlsanity.com/paper/1907.02227/) (2019)
- [Evaluating Gender Bias in Speech Translation](https://mlsanity.com/paper/2010.14465/) (2020)
- [Model-Based Approach for Measuring the Fairness in ASR](https://mlsanity.com/paper/2109.09061/) (2021)
- [Speech Recognition by Simply Fine-tuning BERT](https://mlsanity.com/paper/2102.00291/) (2021)
- [See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers](https://mlsanity.com/paper/2006.06328/) (2020)
- [Deaf, Hard of Hearing, and Hearing Perspectives on using Automatic Speech Recognition in Conversation](https://mlsanity.com/paper/1909.01176/) (2019)

## Built on by (most cited first, 19 of 19)

- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [Evaluating Bias in Spoken Dialogue LLMs for Real-World Decisions and Recommendations](https://mlsanity.com/paper/2510.02352/) (2025)
- [Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants](https://mlsanity.com/paper/2405.19342/) (2024)
- [Automatic Speech Recognition Biases in Newcastle English: an Error Analysis](https://mlsanity.com/paper/2506.16558/) (2025)
- [Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition](https://mlsanity.com/paper/2307.07280/) (2023)
- [Towards spoken dialect identification of Irish](https://mlsanity.com/paper/2307.07436/) (2023)
- [DENOASR: Debiasing ASRs through Selective Denoising](https://mlsanity.com/paper/2410.16712/) (2024)
- [CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese](https://mlsanity.com/paper/2511.04139/) (2025)
- [Big model only for hard audios: Sample dependent Whisper model selection for efficient inferences](https://mlsanity.com/paper/2309.12712/) (2023)
- [Ethio-ASR: Joint Multilingual Speech Recognition and Language Identification for Ethiopian Languages](https://mlsanity.com/paper/2603.23654/) (2026)
- [CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition](https://mlsanity.com/paper/2502.01777/) (2025)
- [ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers](https://mlsanity.com/paper/2308.15742/) (2023)
- [Nollywood: Let's Go to the Movies!](https://mlsanity.com/paper/2407.02631/) (2024)
- [A Survey on Spoken Italian Datasets and Corpora](https://mlsanity.com/paper/2501.06557/) (2025)
- [Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription](https://mlsanity.com/paper/2508.09865/) (2025)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [Convex Low-resource Accent-Robust Language Detection in Speech Recognition](https://mlsanity.com/paper/2605.23235/) (2026)

## Abstract (arXiv)

Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some users, it cannot decode the speech, and any output text is filled with errors that are incomprehensible to the human reader. These systems do not work equally for everyone and actually hinder the productivity of some users. In this paper, we present research that addresses ASR biases against gender, race, and the sick and disabled, while exploring studies that propose ASR debiasing techniques for mitigating these discriminations. We also discuss techniques for designing a more accessible and inclusive ASR technology. For each approach surveyed, we also provide a summary of the investigation and methods applied, the ASR systems and corpora used, and the research findings, and highlight their strengths and/or weaknesses. Finally, we propose future opportunities for Natural Language Processing researchers to explore in the next level creation of ASR technologies.