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This article is devoted to solving the problem of converting sign language into a consistent
text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation.
The paper proposes an improved method of continuous recognition of sign language, the results of
which are transmitted to a natural language processor based on analyzers of morphology, syntax,
and semantics of the Kazakh language, including morphological inflection and the construction
of an intonation model of simple sentences. This approach has significant practical and social
significance, as it can lead to the development of technologies that will help people with disabilities
to communicate and improve their quality of life. As a result of the cross-validation of the model, we
obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation.
We also identified 20 sentence structures of the Kazakh language with their intonational model.
