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• Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter

  Xinru Tang, Jingjin Li, Shaomei Wu · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Tang, Li, and Wu present the first study to push the 'disability-first' principle beyond dataset collection and into the dataset annotation stage of the AI pipeline. Their case is stuttered speech: despite a growing number of stuttering datasets (FluencyBank, UCLASS, KSoF,…

  AI dataset annotation · stuttering · speech recognition · disability-first design · embodied knowledge

• Speech AI for All: The What, How, and Who of Measurement

  Kimi Wenzel, Alisha Pradhan, Maria Teleki, Tobias M. Weinberg, Robin Netzorg, Alyssa Hillary Zisk, Anna Seo Gyeong Choi, Jingjin Li, Raja Kushalnagar, Colin Lea, Abraham Glasser, Christian Vogler, Ly Xinzhen M. Zhangsun Brown, Nan Bernstein Ratner, Allison Koenecke, Karen Nakamura, Shaomei Wu · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26) — Workshop

  This CHI 2026 workshop proposal — the second in the organisers' 'Speech AI for All' series — assembles 17 researchers, practitioners, and community advocates to tackle a specific downstream problem in fair and accessible speech AI: measurement. The motivating claim is that…

  speech AI · automatic speech recognition · speech diversity · augmentative and alternative communication · disfluency

• From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

  Colin Lea, Zifang Huang, Jaya Narain, Lauren Tooley, Dianna Yee, Dung Tien Tran, Panayiotis Georgiou, Jeffrey P. Bigham, Leah Findlater · 2023 · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23)

  This paper investigates how people who stutter (PWS) experience consumer speech recognition systems and demonstrates technical improvements that can significantly reduce errors. The work combines user research with engineering interventions across the speech recognition…

  stuttering · speech recognition · voice assistants · dictation · speech accessibility

• Exploring Smart Speaker User Experience for People Who Stammer

  Anna Bleakley, Daniel Rough, Abi Roper, Stephen Lindsay, Martin Porcheron, Minha Lee, Stuart Alan Nicholson, Benjamin R. Cowan, Leigh Clark · 2022 · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '22)

  This paper investigates how people who stammer experience smart speakers in their daily lives over a three-week period. The researchers deployed Google Nest devices in the homes of 11 participants (mean age 33, recruited through the STAMMA charity in the UK) and collected data…

  stammering · stuttering · speech disfluency · smart speakers · voice interface

• Fluent: An AI Augmented Writing Tool for People who Stutter

  Bhavya Ghai, Klaus Mueller · 2021 · The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2021)

  This paper presents Fluent, a novel AI-powered writing tool designed to help people who stutter (PWS) prepare scripts and written content that they can deliver more fluently. Over 70 million people worldwide stutter, and a common coping strategy is word substitution — replacing…

  stuttering · speech disorders · machine learning · active learning · natural language processing

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