Combining many alignments for speech to speech translation

Alignment combination (symmetrization) has been shown to be useful for improving Machine Translation (MT) models. Most existing alignment combination techniques are based on heuristics, and can combine only two sets of alignments at a time. Recently in [1], we proposed a power mean based algorithm that can be optimized to combine an arbitrary number alignment tables simultaneously. In this paper we present an empirical investigation of the merits of the approach for combining a large number of alignments (more than 200 in total before pruning). The results of the study suggest that the algorithm can often improve the performance of speech to speech translation systems for low resource languages. © 2010 ISCA.