In this paper we introduce a robust classification framework for tongue-movement ear pressure signals based around an ensemble voting methodology. The ensemble members are comprised of different combinations of sensor inputs i.e. two in-ear microphones and an acoustic gel sensor positioned under the chin of the individual and classification using three different base models. It is shown that by using all nine ensemble members when compared to the individual (base) models, the average misclassification rate can be reduced from 23% to 2.8% when using the majority voting strategy. The correct classification rate is improved from 76% to 92.4% when utilizing either the borda count or condorcet methods. This is achieved through a combination of rejection based on ambiguity in the ensemble and diversity in the misclassified instances across the ensemble members.