Recent efforts in spoken language translation
Spoken language translation (SLT) is of great relevance in our increasingly globalized world, both from a social and economic point of view. It is one of the major challenges in automatic speech recognition (ASR) and machine translation (MT), driving an intense research activity in these areas. Speech translation is useful to assist person-to-person communication in limited domains like tourism and traveling and to translate foreign parliamentary speeches and broadcast news. Speech translation is based on a suitable combination of two independent technologies, namely ASR and MT of written language. Thus, the important question is how to pass on the ASR ambiguities to the MT process. A unifying framework for this ASR-MT interface is provided by applying the Bayes decision rule to the speech translation tasks as whole rather than to each task individually. Depending on the MT approaches used, such as finite-state transducers or phrase-based modeling, various types of ASR-MT interfaces have been studied, ranging from N-best lists through word lattices to confusion networks. We have discussed experimental results on various tasks, ranging from limited to unrestricted domains. Despite the significant advances and the large number of experimental studies, it is still an open question what type of interface provides a suitable compromise between translation accuracy and computational cost.