End-to-end speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose SDST , an integral framework withS uccessiveD ecoding for end-to-endS peech-to-textT ranslation task. This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT) , an integral framework for speech-to-text translation. Our method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, TED English-German dataset, and TED English-Chinese dataset . Experiments show that our proposed %DIFDELCMD < \method %%% COSTT outperforms the previous state-of-the-art methods . Our code is available at URL
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