We show how ROVER and confusion network combination (CNC) can be improved with classification. The general idea of improving combination with classification is that each word is assigned to a certain location and at each location a classifier decides which of the provided alternatives is most likely correct. We investigate four variations of this idea and three different classifiers, which are trained on various features derived from ASR lattices. For our experiments, we use highly optimized ROVER and CNC systems as baseline, which already give a relative reduction in WER of more than 20% for the TC-Star 2007 English task. With our methods we can further improve the result of the corresponding standard combination method.
Bibliographic reference. Hoffmeister, Björn / Schlüter, Ralf / Ney, Hermann (2008): "iCNC and iROVER: the limits of improving system combination with classification?", In INTERSPEECH-2008, 232-235.