# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 6 Conclusions  
This study addresses the issue of the lack of parallel data for KSL and presents methods for constructing a parallel corpus using glosses. This represents a significant step in the development of machine translation systems for individuals with hearing and speech impairments in Kazakhstan. Additionally, this study explores the structure of KSL by analyzing its word order in comparison to spoken languages. The data were examined to determine whether a basic word order can be identified in KSL. At the final stage, the research focused on developing a parallel corpus to analyze KSL using NLP and machine learning techniques. A standard text processing pipeline was applied, incorporating tokenization, morphological analysis, and lemmatization, along with sentence categorization. The parser was used to process over 100,000 sentences from books and news sources. A machine translation model was then built using the Transformer architecture, with training parameters outlined in the section \"Parameters and Implementation Details\". The results demonstrated high translation accuracy, highlighting the effectiveness of the proposed approach for processing KSL. This research contributes to the advancement of automated sign language processing systems, paving the way for new communication opportunities for individuals with hearing and speech impairments. The development of a parallel corpus for KSL is a major achievement that expands research possibilities in the fields of machine translation and KSL processing, an area that has been largely unexplored in Kazakhstan. Existing machine learning methods were adapted to account for the unique grammatical and lexical characteristics of KSL, necessitating the creation of specialized rules, including a parser that converts Kazakh text into gloss format. This study also includes an analysis of existing sign language translation systems, identifies their limitations, and demonstrates how the adapted approach enhances translation quality. The developed parallel corpus and accompanying tools, such as a mobile application for translating text into sign language, offer extensive opportunities for the deaf and hard-of-hearing community in Kazakhstan. These resources contribute to the development of educational materials, improved access to information, and greater social integration, emphasizing the importance of this work. It is recommended that future research seek to evaluate the educational impact of the mobile application through the use of user surveys, the analysis of feedback on the App Store, and interviews with individuals with hearing and speech impairments. However, it is essential that these assessments are conducted only after full integration of the system into the mobile application. Such integration will facilitate the collection of meaningful user feedback, thereby ensuring that improvements are made based on actual experiences and needs. Conceptualization, A.Y. and M.S. ; methodology, E.D. ; software, B.S. ; formal analysis, date base, M.O. ; mathematical models, U.B. ; resources, B.S. ; algorithm, mathematic analysis, B.S. and U.B. ; data curation, U.B. ; writing--original draft preparation, M.S. ; writing--review and editing, E.D. and U.B. ; project administration, A.Y. All authors have read and agreed to the published version of the manuscript. This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR24992875). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.