Signing Insights: Developing a Computational Prototype for Pakistani Sign Language Analysis

Abstract

Sign languages are a vital means of communication for the deaf and hard-of-hearing communities, yet they remain under-explored in linguistic research, particularly in Pakistan. This study presents the development of a computational prototype for analyzing Pakistani Sign Language (PSL), focusing on its syntactic and prosodic features. We employ a multimodal approach, integrating corpus linguistics and machine learning techniques to identify patterns in PSL. Our prototype enables the automatic recognition and analysis of PSL signs, providing insights into the language's structure and usage. The findings of this research have implications for language teaching, learning, and accessibility technologies. This study demonstrates the potential of computational methods to advance our understanding of sign languages and promote inclusivity.