Word classification for sign language synthesizer using hidden Markov model
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 pr...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/40305/ http://irep.iium.edu.my/40305/ http://irep.iium.edu.my/40305/ http://irep.iium.edu.my/40305/1/40305.pdf http://irep.iium.edu.my/40305/3/40305-Word%20classification%20for%20sign%20language%20synthesizer%20using%20hidden%20Markov%20model_SCOPUS.pdf |
| Summary: | 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 %. |
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