In this paper we propose a way to cope with questions typed by dyslexic users as they are usually a deformation of the intended query that cannot be corrected with classical spellcheckers. We first propose a new model for statistic question answering systems based on a probabilistic information retrieval model and a combination of results. This model allows a multiple weighted terms query as an input. We also introduce a phonology based approach at the sentence level to derive possible intended terms from typed questions. This approach uses the finite state machine framework to go from phonetic hypothesis and spellchecker proposals to hypothesized sentences thanks to a language model. The final weighted queries are obtained thanks to posterior probabilities computation. They are evaluated according to new density and appearance rating measures which adapt recall and precision to non binary data.