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Bidirectional Kazakh Sign Language prosody-aware translation using computer vision and speech recognition techniques.

Mukhtar Zhassuzak^1,2, Zholdas Buribayev², Maria Aouani²

¹Institute of Information and Computational Technologies CS MSHE RK, Almaty, Kazakhstan.

Frontiers in Artificial Intelligence

|June 3, 2026

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This summary is machine-generated.

This study developed a bidirectional communication system for enhanced interaction between hearing-impaired and hearing individuals using Kazakh Sign Language (KSL) gesture recognition, achieving 92% accuracy.

Area of Science:

Human-Computer Interaction
Artificial Intelligence
Linguistics

Background:

Effective communication between hearing-impaired and hearing individuals remains a significant challenge.
Existing assistive technologies often lack comprehensive bidirectional capabilities.
Automated sign language recognition is crucial for bridging this communication gap.

Purpose of the Study:

To develop and evaluate a bidirectional communication system integrating Kazakh Sign Language (KSL) recognition and speech synthesis.
To enhance interaction and accessibility for deaf and hearing individuals.
To demonstrate a proof-of-concept for automated sign language understanding.

Main Methods:

Integration of KSL gesture detection, closed-set sentence classification, speech recognition, and speech generation.

Keywords:

Liquid Neural Networks human-computer interaction prosody prediction sign language translation speech synthesis

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Training and evaluation using a dataset of KSL gestures (images/video) and Kazakh speech audio.

Utilizing machine learning models for gesture recognition and speech synthesis.

Main Results:

The overall system achieved 92% accuracy in classifying 12 closed-set KSL sentences.
Individual model accuracies were consistently above 87%.
The system demonstrated competitive accuracy compared to existing approaches.

Conclusions:

The proposed system is a feasible proof-of-concept for improving communication accessibility.
Automated sign language understanding can effectively bridge the communication gap.
The technology holds practical applicability for signers and non-signers.