A heuristic Hidden Markov Model to recognize inflectional words in sign system for Indonesian language known as SIBI (Sistem Isyarat Bahasa Indonesia)

Abstract

SIBI (Sistem Isyarat Bahasa Indonesia) is the commonly used sign language in Indonesia. SIBI, which follows Indonesian language's grammatical structure, is a complex and unique sign language. A method to recognize SIBI gestures in a rapid, precise and efficient manner needs to be developed for the SIBI machine translation system. Feature extraction method with space-efficient feature set and at the same time retained its capability to recognize different types of SIBI gestures is the ultimate goal. There are four types of SIBI gestures: root, affix, inflectional and function word gestures. This paper proposed to use heuristic Hidden Markov Model and a feature extraction system to separate inflectional gesture into its constituents, prefix, suffix and root. The separation reduces the amount of feature sets that would otherwise as big as the product of the prefixes, suffixes and root words feature sets of the inflectional word gestures.