Modified 3D-Convolutional Neural Network applied in Dynamic Filipino Sign Language Recognition

Author(s)	Erika T. Bisoy, Chriszel Abby F. Oduca
Country	Philippines
Abstract	Advancing communication for the deaf and mute community requires effective sign language recognition systems. This study improves dynamic gesture recognition for Filipino Sign Language (FSL) using a 3D Convolutional Neural Network (3D CNN). Key challenges such as vanishing and exploding gradients, which hinder the model's learning capabilities, were addressed through batch normalization and gradient clipping. Batch normalization stabilized training by reducing gradient variance from 366.8836 to 4.9723. Gradient clipping minimized instability, leading to increased model accuracy. These techniques significantly enhanced model robustness and generalization. This research highlights their importance in developing accessible FSL recognition systems that promote inclusivity and preserve language.
Keywords	Filipino Sign Language, 3D Convolutional Neural Network, Machine Learning
Field	Computer > Data / Information
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-21
Cite This	Modified 3D-Convolutional Neural Network applied in Dynamic Filipino Sign Language Recognition - Erika T. Bisoy, Chriszel Abby F. Oduca - IJFMR Volume 6, Issue 6, November-December 2024.

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