NSF Convergence Accelerator Track H: Convergent, Human-Centered Design for Making Voice-Activated AI Accessible and Fair to People Who Stutter

说话不流畅是对话中常见的现象，但在口吃的个人中尤其普遍，这是一个全球超过7000万人的社区。有充分的证据表明，口吃的人一直经历着就业歧视、劳动力市场成果下降和社会耻辱。日益普遍的排他性声控人工智能(AI)在设计、培训和测试时没有考虑到与社会规范不同的沟通，这可能会成为口吃者等社区参与日常生活和就业机会的障碍。更糟糕的是，这种技术可能会在就业环境中主动歧视言语不同的人。因此，迫切需要努力减少这些障碍，使有沟通障碍和障碍的人能够充分和公平地使用所有形式的语音识别系统，包括个人语音助理、自动电话界面以及就业准备和招聘软件。该融合加速器项目提出了一种多学科的、受使用启发的方法，该方法利用了人工智能领域的尖端进展，以及对口吃的性质和经验的深入了解，以及更多使用声控系统对法律、道德和劳动力市场的影响。该项目将与不同的利益攸关方合作，开发和分发高影响力的解决方案，以应对一个重大的国家和全球挑战：声控人工智能对不流利的语音的可及性和公平性。提高声控人工智能正确解析和解码不流利语音的能力将提高生活质量、机会平等和机会平等，不仅对口吃者如此，对其他弱势群体和整个社会也是如此，因为所有说话者在某种程度上都不流利。该融合加速器项目的目标是通过开发和实施基于政策、倡导和人工智能的解决方案来解决语音技术的局限性，使语音技术对口吃者来说变得可用和公平。该项目将通过开发包容性培训和测试数据集以及无障碍自动语音识别的注释和开发新的自动语音识别深度学习模式来促进知识的发展。拟议的研究研究将对口吃的性质和体验如何与声控技术中的人工智能可获得性和公平性产生影响和相交的问题建立一个统一和整体的理解，确定口吃者获得现有声控人工智能的障碍和促进者，并评估指导方针和审计工具的有效性。最后，这项活动将吸引多学科和多部门的合作伙伴网络，以确保参与性研究设计，有重点的计划，以招募不同的参与者，广泛传播研究结果，并采用新的、可获得的技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Disfluencies in speech are common artifacts in conversation, but they are especially prevalent in individuals who stutter, a community of more than 70 million people worldwide. It is well-documented that people who stutter consistently experience employment discrimination, diminished labor market outcomes, and societal stigma. The increasingly pervasive use of exclusionary voice-activated artificial intelligence (AI), which are designed, trained, and tested without considering communication that varies from societal norms, can act as a barrier to daily life participation and employment access for communities such as individuals who stutter. Worse, such technology can actively discriminate against people with speech differences in employment contexts. Therefore, there is an immediate and compelling need for efforts to reduce these barriers and empower people with communication differences and disorders to fully and equitably access all forms of speech recognition systems, including personal voice assistants, automated phone interfaces, and job-preparation and hiring software. This Convergence Accelerator project proposes a multidisciplinary, use-inspired approach that leverages cutting-edge advances in AI, as well as deep understanding of the nature and experience of stuttering, and the legal, ethical, and labor market implications of increased use of voice-activated systems. In partnership with diverse stakeholders, the project will develop and distribute high-impact solutions to a major national and global challenge: accessibility and fairness of voice-activated AI for disfluent speech. Improving the ability of voice-activated AI to appropriately parse and decode disfluent speech will increase quality of life, equality of opportunity, and access, not just for people who stutter, but also for other vulnerable populations and for society at large because all speakers are disfluent to some extent.The goal of this Convergence Accelerator project is to resolve limitations in voice technology by developing and implementing policy-, advocacy-, and AI-based solutions to make voice technology accessible and fair to people who stutter. The project will contribute to advancing knowledge through development of inclusive training and test datasets as well as annotation for accessible automatic speech recognition (ASR) and development of novel ASR deep learning models. Proposed research studies will establish a convergent and holistic understanding of how the nature and experience of stuttering impacts and intersects with AI accessibility and fairness in voice-activated technology, identify barriers and facilitators of access to existing voice-activated AI among people who stutter, and evaluate the effectiveness of guidelines and audit tools. Finally, this activity will engage a multidisciplinary and multisectoral network of partners to ensure participatory research design with a focused plan to recruit diverse participants, widespread dissemination of findings, and uptake of new, accessible technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.