KIT’s IWSLT 2021 Offline Speech Translation System
Tuan Nam Nguyen, Thai Son Nguyen, Christian Huber, Ngoc-Quan Pham, Thanh-Le Ha, Felix Schneider, Sebastian Stüker
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This paper describes KIT’submission to the IWSLT 2021 Offline Speech Translation Task. We describe a system in both cascaded condition and end-to-end condition. In the cascaded condition, we investigated different end-to-end architectures for the speech recognition module. For the text segmentation module, we trained a small transformer-based model on high-quality monolingual data. For the translation module, our last year’s neural machine translation model was reused. In the end-to-end condition, we improved our Speech Relative Transformer architecture to reach or even surpass the result of the cascade system.