聚焦非裔英语使用者的语音识别公平性，揭示技术偏见与治理缺失
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
- 从44篇论文中梳理语音技术中的公平性研究框架
- 发现数据采集与模型训练中缺乏包容性实践
- 提出以社区参与为核心的语音识别治理新范式
本系统性文献综述分析了自动语音识别（ASR）及相邻语音语言技术（SLT）在非裔美国人英语（AAE）及其他语言多样性群体中的公平性、偏见与公正问题。基于来自人机交互（HCI）、机器学习/自然语言处理（ML/NLP）和社会语言学领域的44篇同行评审论文，研究识别出四大核心议题：（1）研究人员对ASR相关伤害的理解；（2）涵盖数据采集、整理、标注与模型训练的包容性数据实践；（3）语言包容的方法论与理论路径；（4）促进更公平系统的新兴实践与设计建议。尽管技术公平干预持续增长，但本研究指出治理层面仍存在重大缺口，即缺乏以社区能动性、语言正义与参与问责为中心的框架。为此，我们提出一种以治理为中心的ASR生命周期框架，作为负责任语音识别发展的跨学科新范式，并为研究人员、从业者与政策制定者提供应对语音人工智能中语言边缘化的启示。
原文摘要 · Abstract (English)
This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how researchers understand ASR-related harms; (2) inclusive data practices spanning collection, curation, annotation, and model training; (3) methodological and theoretical approaches to linguistic inclusion; and (4) emerging practices and design recommendations for more equitable systems. While technical fairness interventions are growing, our review highlights a critical gap in governance-centered approaches that foreground community agency, linguistic justice, and participatory accountability. We propose a governance-centered ASR lifecycle as an emergent interdisciplinary framework for responsible ASR development and offer implications for researchers, practitioners, and policymakers seeking to address language marginalization in speech AI systems.
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