TY - GEN
T1 - Speech AI for All
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025
AU - Wu, Shaomei
AU - Wenzel, Kimi
AU - Li, Jingjin
AU - Li, Qisheng
AU - Pradhan, Alisha
AU - Kushalnagar, Raja
AU - Lea, Colin
AU - Koenecke, Allison
AU - Vogler, Christian
AU - Hasegawa-Johnson, Mark
AU - Su, Norman Makoto
AU - Ratner, Nan Bernstein
N1 - This work is partially supported by the National Science Foundation under Grant No. 2427710. We thank Apple Inc. for sponsoring this workshop at CHI 2025.
PY - 2025/4/26
Y1 - 2025/4/26
N2 - Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain.
AB - Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain.
KW - AI FATE
KW - accessibility
KW - automatic speech recognition
KW - disability
KW - speech diversity
KW - speech technology
UR - http://www.scopus.com/inward/record.url?scp=105005733349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105005733349&partnerID=8YFLogxK
U2 - 10.1145/3706599.3706746
DO - 10.1145/3706599.3706746
M3 - Conference contribution
AN - SCOPUS:105005733349
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 26 April 2025 through 1 May 2025
ER -