NSF Convergence Accelerator Track H: An Inclusive, Human-Centered, and Convergent Framework for Transforming Voice AI Accessibility for People Who Stutter

NSF 融合加速器轨道 H：一个包容性、以人为本的融合框架，用于改变口吃者的语音 AI 可访问性

• 批准号: 2345086
• 负责人: Nihar Mahapatra
• 金额: $ 500万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345086&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Inclusive

In a society increasingly navigated through voice-activated artificial intelligence (voice AI), people who stutter-representing over 3 million Americans and roughly 80 million individuals globally-face a major barrier: existing voice AI technologies frequently fail to recognize disfluent speech patterns, leading to a cascade of disadvantages in accessibility, employment, and societal inclusion. This project addresses this pressing concern by transforming voice AI into an inclusive platform that comprehends and respects the diversity of human speech. By reimagining voice AI through the lens of those who stutter, this project not only champions the cause of a historically marginalized group; it also enhances voice technology for everyone, because all speakers are disfluent to some extent. Aligning with NSF's mission, these advancements promote scientific progress, foster national welfare, and contribute to a more inclusive society by embracing and understanding diverse speech patterns.Operationalizing a human-centered and convergent paradigm, this project sets out to accomplish four key synergistic objectives: (1) cultivating a multidisciplinary and multi-sectoral network of stakeholders to steer impactful outcomes, (2) articulating a holistic vision for user-centric voice AI, (3) designing a comprehensive set of adaptive voice AI solutions and establishing a testbed for their evaluation, and (4) drafting guidelines for managing associated voice AI risks. Harnessing cutting-edge AI technology, the project will pioneer inclusive training and test datasets as well as annotation for accessible automatic speech recognition (ASR) and develop advanced ASR deep learning models. The adopted methodology encompasses iterative research, continuous engagement with end-users to pinpoint existing and future barriers, and stringent evaluations of solution efficacy. The overarching goal is to transform voice AI, through an ecosystem that is accessible and responsive to all, thereby ensuring that every voice is heard.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.

在一个越来越多地使用语音激活人工智能（语音AI）的社会中，口吃的人（代表超过300万美国人和全球约8000万人）面临着一个主要障碍：现有的语音AI技术经常无法识别不流利的语音模式，导致在可访问性，就业和社会包容性方面的一系列劣势。该项目通过将语音AI转变为一个包容性平台来解决这一紧迫问题，该平台尊重并尊重人类语音的多样性。通过从口吃者的透镜重新构想语音AI，这个项目不仅支持了一个历史上被边缘化的群体的事业;它还为每个人增强了语音技术，因为所有的说话者在某种程度上都是不流利的。与NSF的使命一致，这些进步促进了科学进步，促进了国家福利，并通过拥抱和理解不同的语言模式为一个更具包容性的社会做出了贡献。该项目以人为本，融合范式，旨在实现四个关键的协同目标：（1）培养多学科和多部门的利益相关者网络，以引导有影响力的成果，（2）阐明以用户为中心的语音人工智能的整体愿景，（3）设计一套全面的自适应语音AI解决方案，并建立一个测试平台进行评估，（4）起草管理相关语音AI风险的指导方针。利用尖端的人工智能技术，该项目将开创包容性的训练和测试数据集，以及无障碍自动语音识别（ASR）的注释，并开发先进的ASR深度学习模型。所采用的方法包括迭代研究、与最终用户持续接触以查明现有和未来的障碍以及对解决方案有效性的严格评估。该奖项的总体目标是通过一个可访问和响应所有人的生态系统来改变语音AI，从而确保每个声音都能被听到。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。