With the purpose of improving Automatic Speech Recognition (ASR) systems performance, many different approaches on combining them have been studied. In this paper, a combination of state a posteriori probabilities given by different feature sets is proposed. In order to perform a coherent combination of state posterior probabilities, the acoustic models trained on different feature sets must have the same topo-logy (i.e. same set of states). For this purpose, a fast and efficient twin model training protocol is proposed. Then, two different strategies for combining probabilities are presented : the linear and the log linear interpolation. By using log linear interpolation, a relative Word Error Rate (WER) reduction of about 15% on MEDIA and 14% on ESTER corpora have been respectively observed.