Advancing Inclusion through Speech Technology with a Bibliometric Study of AI-based Sign Language Recognition

Objective : This paper explores the evolution and scope of research at the intersection of artificial intelligence and assistive speech technologies, with a specific focus on hearing, speech, and language disabilities. It examines how Sign Language Recognition (SLR) systems have advanced as a crucial tool for bridging communication gaps between the hearing-impaired community and the broader auditory society. Data Description : Employing a bibliometric analysis of 3,039 journal articles and conference proceedings indexed in the Scopus database between 1980 and 2024, this study maps the intellectual and technological development of AI-powered SLR. VOSviewer was used for analytical visualization, highlighting publication trajectories, growth phases, geographic research distribution, key contributing nations, author collaboration networks, and keyword co-occurrences. Results : The results indicate that scholarly interest in SLR began around 1985, with a marked acceleration in output since 2016. India and China have emerged as significant contributors to the field. The analysis of keywords and co-citations reveals shifting technological paradigms—from early reliance on Hidden Markov Models and Kinect sensors to more recent applications of deep learning, transfer learning, LSTM networks, attention mechanisms, and transformer architectures. This study contributes to the field of speech technology by offering a comprehensive overview of how AI-driven SLR research is shaping inclusive communication for individuals with speech and hearing impairments.