Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons

Abstract

While there exists a multitude of different sign languages (SLs) across the world, Deaf communities often lack the digital tools required to document and process their languages. In this work, we introduce Mot-Signe (MOSI), an application designed in close collaboration with actors from the French Belgian Deaf community. Our tool enables users to search for French Belgian Sign Language (LSFB) translations or to propose new ones by recording signs themselves. This crowdsourcing approach facilitates the collection of SL data in the wild, enriching the available documentation on LSFB and proposing an innovative response to the data scarcity issue inherent to sign language processing. To evaluate the sustainability of this community-driven data collection, a longitudinal user study was conducted. Following its public release, MOSI demonstrated significant real-world adoption, enabling the collection of over 3,000 distinct LSFB signs. Notably, MOSI captures highly valuable linguistic variations and specialized vocabulary often absent from traditional corpora.

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