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.
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