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This companion paper provides a detailed account of the EMPATH framework [8], which integrates Ensemble Learning, MediaPipe Holistic for gesture tracking, and an Attention-based Transformer model, along with practical insights on designing an effective recognition model. It focuses on the architecture, workflow and offers an in-depth explanation of the interpolation method for handling missing hand keypoints. Additionally, the paper highlights ablation studies on augmentation techniques and key limitations encountered during development and implementation, providing valuable insights for enhancing the reproducibility and performance of word-level sign language recognition using EMPATH.

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Implementation and Reproducibility Notes on EMPATH: Enhancing Word-Level Sign Language Recognition

  • 会议论文

  • 发表日期：

    2026年

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  • Kazi Reyazul Hasan,
  • Muhammad Abdullah Adnan

摘要

This companion paper provides a detailed account of the EMPATH framework [8], which integrates Ensemble Learning, MediaPipe Holistic for gesture tracking, and an Attention-based Transformer model, along with practical insights on designing an effective recognition model. It focuses on the architecture, workflow and offers an in-depth explanation of the interpolation method for handling missing hand keypoints. Additionally, the paper highlights ablation studies on augmentation techniques and key limitations encountered during development and implementation, providing valuable insights for enhancing the reproducibility and performance of word-level sign language recognition using EMPATH.