Enhancing Sign Language Interpretation in Television News: Cognitive Challenges and Technological Innovations

Abstract

Sign language interpretation in television news faces significant challenges due to the rapid pace of information delivery and the fundamental linguistic differences between sign and spoken language. This study aims to identify the obstacles sign language interpreters encountered and examine technological innovations that could improve translation accuracy and accessibility. Employing a Narrative Literature Review (NLR) methodology, the study synthesizes academic literature related to the effectiveness of sign language interpreters in broadcast journalism.  The findings reveal that short-term memory limitations and the inherent complexity of news content significantly influence translation performance. The absence of standardized sign language interpretation practices in television further complicates the delivery of accurate information to deaf and hard-of-hearing audiences. Moreover, nonverbal expressiveness, effective spatial use, and environmental factors—such as lighting and screen layout—play critical roles in the comprehensibility of sign language during broadcasts. This study also emphasizes the urgency of developing AI-based assistive tools and national standards for sign language interpretation in the media sector. It advocates for enhanced interpreter training, especially in handling high-velocity news and technical terminology, and for collaborative initiatives between media institutions and the deaf community to create more inclusive broadcast standards. Ultimately, this research provides theoretical insights through the lens of Information Processing Theory and Cognitive Load Theory and offers practical recommendations to inform policy aimed at inclusive communication. These findings contribute to the broader discourse on improving media accessibility and promoting equity for individuals with hearing disabilities.