The main aim of this paper is to investigate automatic quality assessment for
spoken language translation (SLT). More precisely, we investigate SLT errors
that can be due to transcription (ASR) or to translation (MT) modules...
paper investigates automatic detection of SLT errors using a single classifier
based on joint ASR and MT features. We evaluate both 2-class (good/bad) and
3-class (good/badASR/badMT ) labeling tasks. The 3-class problem necessitates
to disentangle ASR and MT errors in the speech translation output and we
propose two label extraction methods for this non trivial step. This enables -
as a by-product - qualitative analysis on the SLT errors and their origin (are
they due to transcription or to translation step?) on our large in-house corpus
for French-to-English speech translation.