  This paper addresses automatic quality assessment of spoken language
translation (SLT). This relatively new task is defined and formalized as a
sequence labeling problem where each word in the SLT hypothesis is tagged as
good or bad according to a large feature set. We propose several word
confidence estimators (WCE) based on our automatic evaluation of transcription
(ASR) quality, translation (MT) quality, or both (combined ASR+MT). This
research work is possible because we built a specific corpus which contains
6.7k utterances for which a quintuplet containing: ASR output, verbatim
transcript, text translation, speech translation and post-edition of
translation is built. The conclusion of our multiple experiments using joint
ASR and MT features for WCE is that MT features remain the most influent while
ASR feature can bring interesting complementary information. Our robust quality
estimators for SLT can be used for re-scoring speech translation graphs or for
providing feedback to the user in interactive speech translation or
computer-assisted speech-to-text scenarios.
