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The USTC-NEL Speech Translation system at IWSLT 2018

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arxiv 1812.02455 v1 pith:VNN52NEJ submitted 2018-12-06 cs.CL cs.SDeess.AS

Dan Liu , Junhua Liu , Wu Guo , Shifu Xiong , Zhiqiang Ma , Rui Song , Chongliang Wu , Quan Liu This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords systemspeechtranslationiwsltmachinerecognitionmodelstask
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper describes the USTC-NEL 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.

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