KIT ’s IWSLT 2021 Offline Speech Translation System (2021)

Abstract: 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.

## introduction as in previous years, the cascade system's pipeline is constituted by an asr module, a text segmentation module and a machine translation module. in this year's evaluation campaign, we investigated only sequence-to-sequence asr models with three architectures. the segmentation module is basically a monolingual system which translates a disfluent, broken, uncased text (i.e. asr outputs) into a more fluent, written-style text with punctuations in order to match the data conditions of the translation system. the machine translation module's architecture is the same as the previous year's. for the end-to-end system, we improved from our last year's speech relative transformer architecture @xcite . as a result, the end-to-end system can produce better results on certain test sets and approach the performance on some others compared to the cascade system this year, while the end-to-end system was the dominant approach last year. the rest of the paper is organized as followed. section 2 describes the data set used to train and test the system. it is then followed by section 3 providing the description and experimental results of both the cascade and the end-to-end system. in the end, we conclude the paper with section 4. ## offline speech translation we address the offline speech translation task by two main approaches, namely cascade and end-toend. in the cascade condition, the asr module (section 3.1) receives audio inputs and generates raw transcripts, which will then pass through a segmentation module (section 3.2) to formulate well normalized inputs to our machine translation module (section 3.3). the mt outputs are the final outputs of the cascade system. on the other hand, the end-to-end architecture is trained to directly translate english audio inputs into german text outputs (section 3.4). ## conclusion in this year's evaluation campaign, the end-to-end model proves to be a very promising approach since it can compete or even transcend the best cascade model in offline speech translation task. as a note for future work, we would like to investigate two-stage speech translation models @xcite using transformer architectures and compare them with our recent speech translation end-to-end models.