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

本文讨论语音翻译中级联系统带来的挑战以及应对这些挑战的方法，研究表明，直接利用数据进行联合训练无法显著提高级联系统的性能，建议寻找其他有效的方法来提高语音翻译的效率。