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Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech.

Nurzada Amangeldy1, Aru Ukenova1, Gulmira Bekmanova1

  • 1Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan.

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|July 29, 2023
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Summary
This summary is machine-generated.

This study introduces an improved sign language recognition method to convert signs into intonated Kazakh text for speech synthesis. This technology aims to enhance communication accessibility for individuals with disabilities.

Keywords:
intonational speech synthesislong short-term memorynatural language processingsign language recognitionspatiotemporal features

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Area of Science:

  • Computational Linguistics
  • Human-Computer Interaction
  • Speech Technology

Background:

  • Sign language to text conversion is crucial for accessibility.
  • Existing methods lack intonation for natural speech synthesis.
  • Kazakh language processing presents unique morphological and syntactic challenges.

Purpose of the Study:

  • To develop an improved continuous sign language recognition system.
  • To integrate sign language recognition with a Kazakh natural language processor for intonation.
  • To enable voice synthesis of sign language phrases with accurate intonation.

Main Methods:

  • Continuous sign language recognition algorithm.
  • Natural language processing pipeline for Kazakh (morphology, syntax, semantics).
  • Development of an intonation model for simple Kazakh sentences.
  • Cross-validation for model performance evaluation.

Main Results:

  • Achieved an average test accuracy of 0.97.
  • Achieved an average validation accuracy of 0.90.
  • Identified 20 distinct Kazakh sentence structures with associated intonational models.

Conclusions:

  • The proposed method effectively converts sign language to intonated Kazakh text.
  • The system demonstrates high accuracy in recognition and intonation modeling.
  • This technology has significant potential to improve communication for the deaf and hard-of-hearing community.