  Speech translation (ST) is the task of directly translating acoustic speech
signals in a source language into text in a foreign language. ST task has been
addressed, for a long time, using a pipeline approach with two modules : first
an Automatic Speech Recognition (ASR) in the source language followed by a
text-to-text Machine translation (MT). In the past few years, we have seen a
paradigm shift towards the end-to-end approaches using sequence-to-sequence
deep neural network models. This paper presents our efforts towards the
development of the first Broadcast News end-to-end Arabic to English speech
translation system. Starting from independent ASR and MT LDC releases, we were
able to identify about 92 hours of Arabic audio recordings for which the manual
transcription was also translated into English at the segment level. These data
was used to train and compare pipeline and end-to-end speech translation
systems under multiple scenarios including transfer learning and data
augmentation techniques.
