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All Journal IAES International Journal of Robotics and Automation (IJRA)
Haris Al Qodri Maarif
International Islamic University Malaysia
Automotive Engineering Electrical & Electronics Engineering

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Adaptive language processing unit for Malaysian sign language synthesizer Haris Al Qodri Maarif; Teddy Surya Gunawan; Rini Akmeliawati
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i4.pp326-339

Abstract

Language processing unit (LPU) is a system built to process text-based data to comply with the rules of the sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that hearing/impaired speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers time lagging and complexity problems, resulting in high processing time. The two features, i.e., the computational time and success rate, become trade-offs which means the processing time becomes longer to achieve a higher success rate. This paper proposes an adaptive LPU that allows processing the words from spoken words to Malaysian SL grammatical rule that results in relatively fast processing time and a good success r ate. It involves n-grams, natural language processing (NLP) , and hidden Markov models (HMM)/Bayesian networks as the classifier to process the text-based input. As a result, the proposed LPU system has successfully provided an efficient (fast) processing time and a good success rate compared to LPU with other edit distances (mahalanobis, Levenshtein, and soundex). The system has been tested on 130 text-input sentences with several words ranging from 3 to 10 words. Results showed that the proposed LPU could achieve around 1.497ms processing time with an average success rate of 84.23% for a maximum of ten-word sentences.
Co-Authors Rini Akmeliawati Teddy Surya Gunawan