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Augmentative and Alternative Communication System Using Information Priority and Retrieval

In this paper, we propose a new method for applications of Augmentative and Alternative Communication (AAC) systems. These systems aid communication for people with impairments or comprehension issues with spoken or written languages in a dialogue situation. In previous studies, the user selected symbols as substitutes for displayed words and the system generated sentences based on these selections. In this study, our proposed method can generate the required sentence using minimum keystroke input and reduced input errors. Here, the system takes the lead in suggesting symbol candidates to the user. In our experiments, we use a decision tree. This is a machine learning method for automatically setting strategies in order to set the order of the symbol candidates. In our experiments, the proposed method reduces the number of key strokes by 87.6%.