Abstract
Scientific research in the field of automated sign language translation represents a crucial stage in the development of technologies supporting the hearing-impaired and deaf communities. This article presents a comprehensive approach to addressing semantic and technical challenges associated with the uniqueness of sign language. The research goal is to create an innovative system that combines semantic analysis, sign synthesis, and facial mimicry for the most accurate conveyance of emotional context. The study focuses on the unique features of the Kazakh language and cultural contexts that influence sign communication. The research centers on the development of a semantic system capable of adequately interpreting metaphors, idioms, and classifier predicates of sign language. The three-dimensional nature of signs is analyzed, and a solution to the formal description problem is proposed. The article introduces a database, analysis algorithm, and a prototype 3D avatar capable of translating textual data into sign language. Special attention is given to the processing of idioms and variability in expressing emotions in sign language. Utilizing machine learning principles and computational linguistics algorithms, the authors present an integrated approach to sign language translation, considering linguistic, cultural, and emotional aspects. The proposed algorithms and formulas facilitate effective interaction between textual information and sign expression. The research results not only provide an overview of current challenges in automated sign language translation but also offer practical approaches to addressing them. The developed approach could be a key step towards creating more efficient communication systems for the hearing-impaired and deaf. Which in the future may solve numerous issues with Kazakh sign language.