Junhua Liu


2022

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The USTC-NELSLIP Offline Speech Translation Systems for IWSLT 2022
Weitai Zhang | Zhongyi Ye | Haitao Tang | Xiaoxi Li | Xinyuan Zhou | Jing Yang | Jianwei Cui | Pan Deng | Mohan Shi | Yifan Song | Dan Liu | Junhua Liu | Lirong Dai
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes USTC-NELSLIP’s submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese. We describe both cascaded architectures and end-to-end models which can directly translate source speech into target text. In the cascaded condition, we investigate the effectiveness of different model architectures with robust training and achieve 2.72 BLEU improvements over last year’s optimal system on MuST-C English-German test set. In the end-to-end condition, we build models based on Transformer and Conformer architectures, achieving 2.26 BLEU improvements over last year’s optimal end-to-end system. The end-to-end system has obtained promising results, but it is still lagging behind our cascaded models.

2018

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The USTC-NEL Speech Translation system at IWSLT 2018
Dan Liu | Junhua Liu | Wu Guo | Shifu Xiong | Zhiqiang Ma | Rui Song | Chongliang Wu | Quan Liu
Proceedings of the 15th International Conference on Spoken Language Translation

This paper describes the USTC-NEL (short for ”National Engineering Laboratory for Speech and Language Information Processing University of science and technology of china”) system to the speech translation task of the IWSLT Evaluation 2018. The system is a conventional pipeline system which contains 3 modules: speech recognition, post-processing and machine translation. We train a group of hybrid-HMM models for our speech recognition, and for machine translation we train transformer based neural machine translation models with speech recognition output style text as input. Experiments conducted on the IWSLT 2018 task indicate that, compared to baseline system from KIT, our system achieved 14.9 BLEU improvement.