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Data augmentation for pipeline-based speech translation

Diego Alves
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Askars Salimbajevs
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Pipeline-based speech translation methods may suffer from errors found in speech recognition system output. Therefore, it is crucial that machine translation systems are trained to be robust against such noise. In this paper, we propose two methods for parallel data augmentation for pipeline-based speech translation system development. The first method utilises a speech processing workflow to introduce errors and the second method generates commonly found suffix errors using a rule-based method. We show that the methods in combination allow significantly improving speech translation quality by 1.87 BLEU points over a baseline system.
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hal-02907053 , version 1 (27-07-2020)


  • HAL Id : hal-02907053 , version 1


Diego Alves, Askars Salimbajevs, Mārcis Pinnis. Data augmentation for pipeline-based speech translation. 9th International Conference on Human Language Technologies - the Baltic Perspective (Baltic HLT 2020), Sep 2020, Kaunas, Lithuania. ⟨hal-02907053⟩
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