Adaptive language processing unit for Malaysian sign language synthesizer

Haris Al Qodri Maarif, Teddy Surya Gunawan, Rini Akmeliawati


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.


Classifier; Distance algorithm; Malaysian sign language; Natural language processing

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IAES International Journal of Robotics and Automation (IJRA)
ISSN 2089-4856, e-ISSN 2722-2586
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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