Abstract: Effects of the non-context features, such as gender, speaker group identity, speaking rate and channel, for the classification and modeling of the speech data are studied based on data clustering and pre-classification knowledge methods. In order to incorporate non-context features with the context ones in the modeling process, generalized feature decision tree scheme is adopted and extended for the building of multiple high resolution acoustic models. Maximum likelihood model combination is then advanced to solve the subsequent model selection problem. Experimental results on two sets indicated that 8.