@inproceedings{zhou-etal-2023-submission,
title = "Submission of {USTC}{'}s System for the {IWSLT} 2023 - Offline Speech Translation Track",
author = "Zhou, Xinyuan and
Cui, Jianwei and
Ye, Zhongyi and
Wang, Yichi and
Xu, Luzhen and
Zhang, Hanyi and
Zhang, Weitai and
Dai, Lirong",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.15",
doi = "10.18653/v1/2023.iwslt-1.15",
pages = "194--201",
abstract = "This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.",
}
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<abstract>This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.</abstract>
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%0 Conference Proceedings
%T Submission of USTC’s System for the IWSLT 2023 - Offline Speech Translation Track
%A Zhou, Xinyuan
%A Cui, Jianwei
%A Ye, Zhongyi
%A Wang, Yichi
%A Xu, Luzhen
%A Zhang, Hanyi
%A Zhang, Weitai
%A Dai, Lirong
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (in-person and online)
%F zhou-etal-2023-submission
%X This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.
%R 10.18653/v1/2023.iwslt-1.15
%U https://aclanthology.org/2023.iwslt-1.15
%U https://doi.org/10.18653/v1/2023.iwslt-1.15
%P 194-201
Markdown (Informal)
[Submission of USTC’s System for the IWSLT 2023 - Offline Speech Translation Track](https://aclanthology.org/2023.iwslt-1.15) (Zhou et al., IWSLT 2023)
ACL
- Xinyuan Zhou, Jianwei Cui, Zhongyi Ye, Yichi Wang, Luzhen Xu, Hanyi Zhang, Weitai Zhang, and Lirong Dai. 2023. Submission of USTC’s System for the IWSLT 2023 - Offline Speech Translation Track. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 194–201, Toronto, Canada (in-person and online). Association for Computational Linguistics.