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Currently, in speech translation, the straightforward approach - cascading arecognition system with a translation system - delivers state-of-the-artresults. However, fundamental challenges such as error propagation from theautomatic speech recognition system still remain. To mitigate these problems,recently, people turn their attention to direct data and propose various jointtraining methods. In this work, we seek to answer the question of whether jointtraining really helps cascaded speech translation. We review recent papers onthe topic and also investigate a joint training criterion by marginalizing thetranscription posterior probabilities. Our findings show that a strong cascadedbaseline can diminish any improvements obtained using joint training, and wesuggest alternatives to joint training. We hope this work can serve as arefresher of the current speech translation landscape, and motivate research infinding more efficient and creative ways to utilize the direct data for speechtranslation.

Does Joint Training Really Help Cascaded Speech Translation?