The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation

• URL: http://arxiv.org/abs/2108.03845v1
• Date: Mon, 9 Aug 2021 07:28:04 GMT
• Title: The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
• Authors: Minghan Wang, Yuxia Wang, Chang Su, Jiaxin Guo, Yingtao Zhang, Yujia Liu, Min Zhang, Shimin Tao, Xingshan Zeng, Liangyou Li, Hao Yang, Ying Qin
• Abstract summary: This paper describes our work in participation of the IWSLT-2021 offline speech translation task. Our system was built in a cascade form, including a speaker diarization module, an Automatic Speech Recognition (ASR) module and a Machine Translation (MT) module. Our method achieves 24.6 BLEU score on the 2021 test set.
• Score: 22.617563646374602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our work in participation of the IWSLT-2021 offline speech translation task. Our system was built in a cascade form, including a speaker diarization module, an Automatic Speech Recognition (ASR) module and a Machine Translation (MT) module. We directly use the LIUM SpkDiarization tool as the diarization module. The ASR module is trained with three ASR datasets from different sources, by multi-source training, using a modified Transformer encoder. The MT module is pretrained on the large-scale WMT news translation dataset and fine-tuned on the TED corpus. Our method achieves 24.6 BLEU score on the 2021 test set.

Related papers

• CMU's IWSLT 2024 Simultaneous Speech Translation System [80.15755988907506]
  This paper describes CMU's submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder.
  arXiv  Detail & Related papers  (2024-08-14T10:44:51Z)
• Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024 [61.189875635090225]
  Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST)
  arXiv  Detail & Related papers  (2024-06-24T16:38:17Z)
• KIT's Multilingual Speech Translation System for IWSLT 2023 [58.5152569458259]
  We describe our speech translation system for the multilingual track of IWSLT 2023. The task requires translation into 10 languages of varying amounts of resources. Our cascaded speech system substantially outperforms its end-to-end counterpart on scientific talk translation.
  arXiv  Detail & Related papers  (2023-06-08T16:13:20Z)
• Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23 [0.0]
  This paper describes the submission of the UPC Machine Translation group to the IWSLT 2023 Offline Speech Translation task. Our Speech Translation systems utilize foundation models for speech (wav2vec 2.0) and text (mBART50) We incorporate a Siamese pretraining step of the speech and text encoders with CTC and Optimal Transport, to adapt the speech representations to the space of the text model.
  arXiv  Detail & Related papers  (2023-06-02T07:48:37Z)
• The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task [92.5087402621697]
  This paper describes the submission of our end-to-end YiTrans speech translation system for the IWSLT 2022 offline task. The YiTrans system is built on large-scale pre-trained encoder-decoder models. Our final submissions rank first on English-German and English-Chinese end-to-end systems in terms of the automatic evaluation metric.
  arXiv  Detail & Related papers  (2022-06-12T16:13:01Z)
• The USYD-JD Speech Translation System for IWSLT 2021 [85.64797317290349]
  This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task. We trained our models with the officially provided ASR and MT datasets. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning.
  arXiv  Detail & Related papers  (2021-07-24T09:53:34Z)
• The IWSLT 2021 BUT Speech Translation Systems [2.4373900721120285]
  BUT's English to German offline speech translation(ST) systems developed for IWSLT 2021. They are based on jointly trained Automatic Speech Recognition-Machine Translation models. Their performances is evaluated on MustC-Common test set.
  arXiv  Detail & Related papers  (2021-07-13T15:11:18Z)
• The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task [23.008938777422767]
  This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task. We use the Transformer-based model architecture and enhance it by Conformer, relative position encoding, and stacked acoustic and textual encoding. We achieve 33.84 BLEU points on the MuST-C En-De test set, which shows the enormous potential of the end-to-end model.
  arXiv  Detail & Related papers  (2021-07-06T07:45:23Z)
• UPC's Speech Translation System for IWSLT 2021 [2.099922236065961]
  This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Our submission is an end-to-end speech translation system, which combines pre-trained models with coupling modules between the encoder and decoder.
  arXiv  Detail & Related papers  (2021-05-10T17:04:11Z)
• DiDi's Machine Translation System for WMT2020 [51.296629834996246]
  We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model. As a result, our submission achieves a BLEU score of $36.6$ in Chinese->English.
  arXiv  Detail & Related papers  (2020-10-16T06:25:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.