论文标题
技术报告：但是语音翻译系统
A Technical Report: BUT Speech Translation Systems
论文作者
论文摘要
本文描述了But的语音翻译系统。这些系统是英语$ \ longrightArrow $德语脱机语音翻译系统。系统基于我们以前的作品\ cite {introly_trained_transformers}。尽管端到端和级联〜（ASR-MT）口语翻译〜（SLT）系统正在达到可比性的性能，但与Oracle输入文本相比，在翻译ASR假设时会观察到大降解。为了减少这种性能降低，我们以ASR目标作为辅助损失共同训练了ASR和MT模块。两个网络均通过神经隐藏表示形式连接。该模型在最终目标函数方面具有端到端可区分的路径，还利用ASR目标进行更好的优化。在推断期间，两个模块（即ASR和MT）通过与N-最佳假设相对应的隐藏表示连接。与经过独立训练的ASR和MT模型的结合进一步提高了系统的性能。
The paper describes the BUT's speech translation systems. The systems are English$\longrightarrow$German offline speech translation systems. The systems are based on our previous works \cite{Jointly_trained_transformers}. Though End-to-End and cascade~(ASR-MT) spoken language translation~(SLT) systems are reaching comparable performances, a large degradation is observed when translating ASR hypothesis compared to the oracle input text. To reduce this performance degradation, we have jointly-trained ASR and MT modules with ASR objective as an auxiliary loss. Both the networks are connected through the neural hidden representations. This model has an End-to-End differentiable path with respect to the final objective function and also utilizes the ASR objective for better optimization. During the inference both the modules(i.e., ASR and MT) are connected through the hidden representations corresponding to the n-best hypotheses. Ensembling with independently trained ASR and MT models have further improved the performance of the system.
