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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

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