Word classification for sign language synthesizer using hidden Markov model

Maarif, Haris Al Qodri and Akmeliawati, Rini and Htike@Muhammad Yusof, Zaw Zaw and Gunawan, Teddy Surya (2014) Word classification for sign language synthesizer using hidden Markov model. In: 5th International Conference on Information & Communication Technology for The Muslim World (ICT4M 2014), 17th--19th November 2014, Kuching, Sarawak.

	PDF - Published Version Restricted to Repository staff only Download (339kB) | Request a copy
	PDF (SCOPUS) - Published Version Restricted to Repository staff only Download (80kB) | Request a copy

Abstract

Sign Language Synthesizer is an algorithm developed to provide signing animation from verbal/spoken language. Word classification in Natural Language Processing (NLP) is required to determine grammatically processed sentences for sign language synthesizer. The correct word position of output can provide understanding to users who use sign language synthesizer tools. In this paper, the Hidden Markov Model is proposed and implemented to process the words and locate their corresponding position correctly. The classification was done for Malay language and has resulted in an average accuracy of 74.67 %.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	5806/40305 (ISBN: 978-147996242-6, DOI: 10.1109/ICT4M.2014.7020617)
Uncontrolled Keywords:	NLP; Hidden Markov Model; Simple Word
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	29 Dec 2014 09:52
Last Modified:	24 Aug 2017 15:30
URI:	http://irep.iium.edu.my/id/eprint/40305

Actions (login required)

View Item