Extracting clues from human interpreter speech for spoken language translation

In previous work, we reported dramatic improvements in automatic speech recognition (ASR) and spoken language translation (SLT) gained by applying information extracted from spoken human interpretations. These interpretations were artificially created by collecting read sentences from a clean parallel text corpus. Real human interpretations are significantly different. They suffer from frequent synopses, omissions and self-corrections. Expressing these differences in BLEU score by evaluating human interpretations with carefully created human translations, we found that human interpretations perform two to three times worse than state-of-the art SLT. Facing these stark differences, we address the question if and how ASR and SLT can profit from human interpretations. In the following we describe initial experiments that apply knowledge derived from real human interpretations for improving English and Spanish ASR and SLT. Our experiments are conducted on a small European Parliamentary Plenary Sessions development set.