In this paper we present a novel ASR system combination technique able to combine systems producing word graphs of different structure and with different segmentations. The new method is based on the definition of a time frame-wise word error cost function in a minimum Bayes risk framework. In contrast to confusion network combination (CNC), it preserves both the word graph structure and the word boundaries.

First experimental results are presented on the European Parliament Plenary Sessions (EPPS) task for European Spanish and British English. The new approach to system combination is compared to both ROVER and CNC. In addition, we also apply data-driven weighting schemes for all system combination approaches addressed in this work. For the experiments presented, a variety of internal systems as well as an additional external system were combined.