A vector-based computational framework for comparative analysis of Kenyan Sign Language and American Sign Language phonology

This paper introduces a new vector-space computational approach for comparative phonology of Kenyan Sign Language (KSL) and American Sign Language (ASL) using minimal pairs contrasts. Using formal SignWriting (FSW) notation, the framework compiles handshape, location, movement, orientation and non-manual features into structured phonological vectors. These vectors enable quantitative assessment of phonological distance between KSL and ASL signs that correspond over matched items in the lexicon. We calculate pairwise, phonologically-based component and global Euclidean distances for a set of minimal pairs, pointing to systematic differences in phonological organisation of the glosses in the dataset. This research highlights the importance of language specific models in sign recognition systems because direct model transfer from ASL to KSL yields significant representational mismatches. Centering on a dual symbolic and geometric representation of sign language data, this work presents a scalable, linguistically motivated approach to cross-linguistic sign analysis that has the potential to find uses in natural language processing, low-resource language modelling and accessible sign technologies.