Article Per Year (5 Year)
p-Index From 2019 - 2024
2.663
P-Index
This Author published in this journals
All Journal Jurnal Teknik ITS JUTI: Jurnal Ilmiah Teknologi Informasi Geoid - Journal of Geodesy and Geomatics Journal of Applied Geospatial Information JURNAL ILMIAH GEOMATIKA Sewagati
Hidayat, Husnul
Departemen Teknik Geomatika Institut Teknologi Sepuluh Nopember
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JUTI: Jurnal Ilmiah Teknologi Informasi

 1 
IMPROVED LIP-READING LANGUAGE USING GATED RECURRENT UNITS Zulfa, Nafa; Suciati, Nanik; Hidayati, Shintami Chusnul
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1080

Abstract

Lip-reading is one of the most challenging studies in computer vision. This is because lip-reading requires a large amount of training data, high computation time and power, and word length variation. Currently, the previous methods, such as Mel Frequency Cepstrum Coefficients (MFCC) with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with LSTM, still obtain low accuracy or long-time consumption because they use LSTM. In this study, we solve this problem using a novel approach with high accuracy and low time consumption. In particular, we propose to develop lip language reading by utilizing face detection, lip detection, filtering the amount of data to avoid overfitting due to data imbalance, image extraction based on CNN, voice extraction based on MFCC, and training model using LSTM and Gated Recurrent Units (GRU). Experiments on the Lip Reading Sentences dataset show that our proposed framework obtained higher accuracy when the input array dimension is deep and lower time consumption compared to the state-of-the-art.
Co-Authors ABDUL MUNIF Adnan Erlangga Raharja Agung Budi Cahyono Agung Budi Cahyono Ahmad Solihuddin Al Ayyubi Ahmadiyah, Adhatus Solichah Anggraini, Ratih Nur Esti Ari Santoso Bangun Muljo Sukojo, Bangun Muljo Budisusanto, Yanto Citra Ayu Sekar Kinasih Dean Ahmed Falahesa Dini Adni Navastara, Dini Adni Dini Adni Navastra Erika Azzahra Filsa Bioresita Ghozy Shalahuddin Kholiq Hepi Hapsari Handayani Ilyas Ilyas Jaelani, Lalu Muhamad Kelly Rossa Sungkono Mohammad Avicenna Mohammad Avicenna Muhammad Aldila Syariz Muhammad Iqbal Izzul Haq Nanik Suciati Navinda Meutia Nisrina Ulfah Nurul Fajrin Ariyani Nurwatik Nurwatik Riyanarto Sarno Sultan Alifian Hapriansyah Sultan Alifian Hapriansyah Teguh Hariyanto, Teguh Zulfa, Nafa