4 papers analyzed
These studies suggest that we study and model the structure and processing of sign languages using deep neural networks, computational models for synthesis, systems combining MediaPipe holistic, graph neural networks, and Electra transformer, and by employing sequential segments for better representation.
The study and modeling of sign languages involve understanding their unique structural and processing characteristics. Sign languages use visual-manual modality rather than auditory-vocal, making their analysis distinct from spoken languages. Researchers employ various methodologies to recognize, synthesize, and interpret sign languages, addressing challenges such as inter-signer variability and the need for accurate computational models.
Signer-Independent Models:
Computational Models for Sign Language:
Use of Advanced Technologies:
Phonetic Representation and Structural Analysis:
The study and modeling of sign languages leverage signer-independent models, advanced computational techniques, and detailed structural analysis. These approaches collectively enhance the recognition, synthesis, and interpretation of sign languages, addressing key challenges such as inter-signer variability and the need for accurate phonetic representation.
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