In this paper, two adaptation schemes are presented: SMAP and SMLLR. Both methods update the parameters of the acoustic models of a speaker independent system in order to improve its performances for a new speaker. We experimented SMAP and SMLLR to HMMs of the ESPERE engine in the batch mode and in the unsupervised incremental mode. The HMMs were learned on the Resource Management corpus. Results of the batch adaptation show a greatest efficienty of SMAP. For the unsupervised incremental adaptation, SMLLR is more powerful than SMAP, according to the incremental scheme we choose.