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.

本研究针对自动语音识别的性别、种族、病残等偏见现象，探讨为缓解这些歧视提出的去偏见技术，并讨论设计更具通用性和包容性的语音识别技术。对于每种探讨方法，本文提供研究摘要、应用方法和研究结果，并强调其优点和 / 或缺点。最后，还提出了自然语言处理研究人员应在下一级别的 ASR 技术创新方面探索的未来机会。

自动语音识别系统为什么不更具包容性？：自动语音识别系统的偏见及其缓解方法。一篇文献综述

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

Responsible development of technology involves applications being inclusive
of the diverse set of users they hope to support. An important part of this is
understanding the many ways to refer to a person and being able to fluently
change between the different forms as needed. We perform a case study on the
singular they, a common way to promote gender inclusion in English. We define a
re-writing task, create an evaluation benchmark, and show how a model can be
trained to produce gender-neutral English with <1% word error rate with no
human-labeled data. We discuss the practical applications and ethical
considerations of the task, providing direction for future work into inclusive
natural language systems.

本研究关注技术的负责任发展，着眼于包容其希望支持的不同用户群体。在此基础上，对英文中一种常见的提倡性别包容的表达方式 —— 单数 they 展开案例研究。研究通过定义重写任务，创建评估基准，构建模型等方式，成功实现了语言性别中立，无需人工标记数据即可达到小于 1% 的错误率。此外，研究探讨了该任务的实际应用和伦理考虑，并提出了未来研究的方向，旨在推动包容性自然语言系统的发展。