  To transcribe speech, automatic speech recognition systems use statistical
methods, particularly hidden Markov model and N-gram models. Although these
techniques perform well and lead to efficient systems, they approach their
maximum possibilities. It seems thus necessary, in order to outperform current
results, to use additional information, especially bound to language. However,
introducing such knowledge must be realized taking into account specificities
of spoken language (hesitations for example) and being robust to possible
misrecognized words. This document presents a state of the art of these
researches, evaluating the impact of the insertion of linguistic information on
the quality of the transcription.
