Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation

TACL 2019 Matthias SperberGraham NeubigJan NiehuesAlex Waibel

Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech... (read more)

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