Project overview
Hong Kong Sign Language (HKSL) is relatively new and undocumented, with barely any written record prior to 1935. The Deaf culture, with no written tradition, relies on one-to-one (“oral”) transmission for survival. Documentation is a prerequisite to studying and preserving the sign language. The collected data acts as a reference resource and preservation effort.
By documenting HKSL and its visual characteristics, this project, entitled “The Sound of Silence: A Journey through Deaf Culture in Hong Kong”, aims to preserve HKSL and the intangible Deaf culture by facilitating the teaching and learning of HKSL, preserving intangible cultural heritage for an illiterate cultural minority, promoting a sense of ownership and legitimacy and connecting the hering and Deaf community through outreach efforts.
Research Outcome
The project aims to document and preserve Hong Kong Sign Language (HKSL) and Deaf culture through the development of an AI-powered handshape detection model and inclusive educational curricula. Key outcomes include the creation of a digital repository and learning platform hosted at https://hksign.atlab.hku.hk/, which facilitates HKSL acquisition for the public while empowering the Deaf community through digital literacy. The research is expected to enhance social inclusivity and provide a new technological framework for the preservation of intangible sign language heritage.
About the researcher
Youngah Do is an Associate Professor and the Acting Chair at the Department of Linguistics, the University of Hong Kong. As a phonologist, her research centers on the acquisition, representation, and processing of sound systems across languages. she aims to formulate hypotheses about the cognitive mechanisms underlying phonological learning and processing by examining cross-linguistic sound patterns. To accomplish this, she conducts experimental studies with human participants and utilizes computational modeling techniques to investigate how the human mind encodes and applies phonological structures.
Publications
Tan, F. L. H., Chik, A. W. C., Thompson, A. L., Yip, J. W. T., & Do, Y. (2026). Machine-learning errors in Hong Kong Sign Language handshape recognition reflect markedness patterns attested in learning. Sign Language & Linguistics. https://doi.org/10.1075/sll.24011.tan
Thompson, A. L., Chik, W. C. A., Ngai, Y. O. M., Chen, P. C. R., Ng, C. Y. J., & Do, Y. (2026). Iconicity and semantic transparency in Hong Kong Sign Language: evidence from ratings and three guessing paradigms. Language and Cognition, 18. https://doi.org/10.1017/langcog.2026.10070
Thompson, A. L., Van Hoey, T., Chik, A. W. C., & Do, Y. (2025). Iconic hand gestures from ideophones exhibit stability and emergent phonological properties: an iterated learning study. Cognitive Linguistics, 36(2), 227–259. https://doi.org/10.1515/cog-2024-0033
Fund Source
N/A
For inquiries, please contact
atlab@hku.hk
For more information, please refer to the website
Hong Kong Sign Language – LDL | Department of Linguistics | HKU