Sign to speech convolutional neural network-based Filipino sign language hand gesture recognition system

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creativework.keywordsconvolutional neural network; Filipino sign language recognition; inflated 3D
dc.contributor.authorJarabese, Mark Benedict D.
dc.contributor.authorMarzan, Charlie S.
dc.contributor.authorBoado, Jenelyn Q.
dc.contributor.authorLopez, Rushaine Rica Mae F.
dc.contributor.authorOfiana, Lady Grace B.
dc.contributor.authorPilarca, Kenneth John P.
dc.date.accessioned2026-04-14T05:59:44Z
dc.date.available2026-04-14T05:59:44Z
dc.date.issued2021
dc.description.abstractSign Language Recognition is a breakthrough for helping deaf-mute people and has been studied for many years. Unfortunately, every research has its own limitation and are still unable to be used commercially. In this study, we developed a real-time Filipino sign language hand gesture recognition system based on Convolutional Neural Network. A manually gathered dataset consists of 237 video clips with 20 different gestures. This dataset underwent data cleaning and augmentation using image pre-processing techniques. The Inflated 3D convolutional neural network was used to train the Filipino sign language recognition model. The experiments considered retraining the pretrained model with top layers and all layers. As a result, the model retrained with all layers using imbalanced dataset was shown to be more effective and achieving accuracy up to 95% over the model retrained with top layers to classify different signs or hand gestures. Using the Rapid Application Development model, the Filipino sign language recognition application was developed and assessed its usability by the target users. With different parameters used in the evaluation, the application found to be effective and efficient.
dc.identifier.citationJarabese, M. B. D., Marzan, C. S., Boado, J. Q., Lopez, R. R. M. F., Ofiana, L. G. B., & Pilarca, K. J. P. (2021). Sign to speech convolutional neural network-based Filipino sign language hand gesture recognition system. 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC). 147–153. DOI 10.1109/ISCSIC54682.2021.00036
dc.identifier.doiDOI 10.1109/ISCSIC54682.2021.00036
dc.identifier.isbn978-1-6654-1627-6
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1299
dc.language.isoen
dc.publisher2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)
dc.relation.urihttps://ieeexplore.ieee.org/document/9644284
dc.relation.urihttps://www.semanticscholar.org/paper/Sign-to-Speech-Convolutional-Neural-Network-Based-Jarabese-Marzan/92d9ce5b6f71621660a04ad7bb28588a33d0b05f
dc.sdgSDG 4
dc.sdgSDG 10
dc.sdgSDG 9
dc.sdgSDG 3
dc.subjectFilipino Sign Language
dc.subjectSign-to-speech system
dc.subjectConvolutional neural network
dc.subjectHand gesture recognition
dc.subjectArtificial intelligence
dc.subjectAssistive technology
dc.subjectComputer vision
dc.subjectSign Language
dc.subjectHand Gestures
dc.subjectHand Gesture Recognition
dc.subjectConvolutional Neural Network
dc.subjectDeveloped Model
dc.subjectClean Data
dc.subjectVideo Clips
dc.subjectSign Language Recognition
dc.subjectModel Performance
dc.subjectTraining Set
dc.subjectDeep Learning
dc.subjectImage Processing
dc.subjectValidation Set
dc.subjectRecognizable
dc.subjectComputer Vision
dc.subjectModel Building
dc.subjectTransfer Learning
dc.subjectSeries Of Images
dc.subjectProcessing Software
dc.subjectOptical Flow
dc.subjectAmerican Sign Language
dc.subjectDeaf People
dc.subjectLower Learning Rate
dc.subjectApplying Transfer Learning
dc.subjectDeaf Community
dc.subjectValidation Accuracy
dc.subjectCNN Model
dc.subjectVideo Editing
dc.subject.ddcArtificial intelligence
dc.subject.ddcPattern recognition
dc.subject.ddcSpeech processing
dc.subject.ddcEducation of the deaf
dc.subject.lcshSign language recognition
dc.subject.lcshFilipino Sign Language
dc.subject.lcshHand gestures
dc.subject.lcshSpeech processing systems
dc.subject.lcshArtificial intelligence
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshAssistive computer technology
dc.titleSign to speech convolutional neural network-based Filipino sign language hand gesture recognition system
dc.typeArticle
oaire.citation.endPage153
oaire.citation.startPage147
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