Abstract:
Kazakh Sign Language (KSL) is a crucial communication tool for individuals
with hearing and speech impairments. Deep learning, particularly Transformer models,
offers a promising approach to improving accessibility in education and communication.
This study analyzes the syntactic structure of KSL, identifying its unique grammatical
features and deviations from spoken Kazakh. A custom parser was developed to convert
Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using
this resource, a Transformer-based machine translation model was trained, achieving high
translation accuracy and demonstrating the feasibility of this approach for enhancing
communication accessibility. The research highlights key challenges in sign language
processing, such as the limited availability of annotated data. Future work directions
include the integration of video data and the adoption of more comprehensive evaluation
metrics. This paper presents a methodology for constructing a parallel corpus through
gloss annotations, contributing to advancements in sign language translation technology.
