# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 1 Introduction  
According to the World Health Organization (WHO), the global population of deaf individuals reached approximately 466 million in 2023, with projections indicating an increase to 900 million by 2050 [1]. \"Hearing loss\" is a broad term encompassing conditions ranging from mild to severe impairments. For many affected individuals, sign language serves as their primary mode of communication. However, they often face challenges in interacting with the broader society. In Kazakhstan, according to data from the Ministry of Labor and Social Protection, the number of officially recognized individuals with disabilities stood at 715,157 as of May 2022 [2]. Sign language is a visual communication system that relies on hand gestures, facial expressions, and body movements to convey information. It is widely used by deaf and hard-of-hearing individuals, enabling them to communicate with each other and, in some cases,with hearing individuals who do not understand sign language [3]. Most machine translation systems primarily focus on spoken languages, leaving translation between spoken and signed languages significantly underdeveloped. Unlike spoken languages, sign language is a visual-spatial system that conveys meaning through a combination of hand movements, finger positions, facial expressions, and body postures in three-dimensional space. This fundamental difference makes the process of translating written text into sign language particularly challenging, as there is no universally standardized grammatical structure across sign languages. Despite these challenges, various strategies exist for converting text into sign language, where the written text serves as the source and the documented sign language representation as the output. Studies [4; 5; 6; 7; 8; 9; 10] explore various approaches to sign language machine translation, including rule-based methods, machine learning techniques, avatar-based translation, and the development of sign language corpora. A key challenge in translating written language into sign language is the lack of uniform grammatical structures, which complicates the process and limits translation accuracy. However, these methods play a crucial role in improving communication accessibility, allowing sign language users to engage more effectively in today's information-driven society. The development and training of a parallel corpus for KSL is a crucial research area that significantly enhances communication and fosters the integration of individuals with hearing and speech impairments into Kazakh society. Sign language serves as a primary mode of communication for those with limited ability to hear and produce spoken language. In Kazakhstan, as in many other countries, individuals with hearing and speech impairments face challenges in education, access to information, and social integration. These difficulties stem from the limited awareness and dissemination of knowledge about KSL, as well as the lack of research and resources dedicated to its study and development. Research initiatives in KSL aim to address these challenges by ensuring that deaf and deafblind individuals have access to education and information while deepening the understanding of their cultural and linguistic backgrounds. The development of a parallel KSL corpus and the advancement of KSL instruction have the potential to serve as valuable tools for researchers, educators, and professionals working with deafblind individuals. These efforts contribute to the creation of new KSL teaching methodologies, the development of translation resources, and the expansion of linguistic knowledge of sign language. This section of the paper reviews the key motivations and challenges faced by researchers in the fields of KSL and parallel corpus development.