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

Michigan State University, East Lansing MI

Investigators

Abstract

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 access, and seeking gainful employment. This project addresses this challenge by transforming voice AI into an integrated platform that comprehends and respects the variations of human speech. By reimagining voice AI through the lens of those who stutter, this project enhances voice technology for all Americans, because all speakers are disfluent to some extent. 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 training and test datasets as well as annotation for user-friendly 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 technology efficacy. The overarching goal is to transform voice AI, through an ecosystem that is 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.