We present an adaptation of the Text-to-Picto system, initially designed for Dutch, and extended to English and Spanish. The original system, aimed at people with an intellectual disability, automatically translates text into pictographs (Sclera and Beta). We extend it to French and add a large set of Arasaac pictographs linked to WordNet 3.1. To carry out this adaptation, we automatically link the pictographs and their metadata to synsets of two French WordNets and leverage this information to translate words into pictographs. We automatically and manually evaluate our system with different corpora corresponding to different use cases, including one for medical communication between doctors and patients. The system is also compared to similar systems in other languages.

This article investigates the evaluation of a word prediction system in an Augmentative and Alternative Communication (AAC) software for disabled people. In addition to having a reduced mobility, these users have an altered use of speech that must be compensated by a technological aid offering input methods adapted to their capabilities. To improve their communication speed, different prediction and language modeling techniques are used. We present the parameterization of statistical predictors. Their configuration in French is evaluated by a simulator and tested by a disabled person. The results show that a language model built from a large literary corpus saves more than one keystroke out of two, the performance of these systems varying according to several parameters.