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Speech translation (ST) aims to learn transformations from speech in thesource language to the text in the target language. Previous works show thatmultitask learning improves the ST performance, in which the recognitiondecoder generates the text of the source language, and the translation decoderobtains the final translations based on the output of the recognition decoder.Because whether the output of the recognition decoder has the correct semanticsis more critical than its accuracy, we propose to improve the multitask STmodel by utilizing word embedding as the intermediate.

Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation