Hendrik Pesch


2018

pdf
Neural Speech Translation at AppTek
Evgeny Matusov | Patrick Wilken | Parnia Bahar | Julian Schamper | Pavel Golik | Albert Zeyer | Joan Albert Silvestre-Cerda | Adrià Martínez-Villaronga | Hendrik Pesch | Jan-Thorsten Peter
Proceedings of the 15th International Conference on Spoken Language Translation

This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.