Word Confidence Estimation (WCE) for machine translation (MT) or automatic speech recognition (ASR) consists in judging each word in the (MT or ASR) hypothesis as correct or incorrect by tagging it with an appropriate label. In the past, this task has been treated separately in ASR or MT contexts and we propose here a joint estimation of word confidence for a spoken language translation (SLT) task involving both ASR and MT. This research work is possible because we built a specific corpus which is first presented. This corpus contains 2643 speech utterances for which a quintuplet containing: ASR output (src-asr), verbatim transcript (src-ref), text translation output (tgt-mt), speech translation output (tgt-slt) and post-edition of translation (tgt-pe), is made available. The rest of the paper illustrates how such a corpus (made available to the research community) can be used for evaluating word confidence estimators in ASR, MT or SLT scenarios. WCE for SLT could help rescoring SLT output graphs, improving translators productivity (for translation of lectures or movie subtitling) or it could be useful in interactive speech-to-speech translation scenarios.

This paper is a description of the system presented by the LIG laboratory to the IWSLT08 speech translation evaluation. The LIG participated, for the second time this year, in the Arabic to English speech translation task. For translation, we used a conventional statistical phrase-based system developed using the moses open source decoder. We describe chronologically the improvements made since last year, starting from the IWSLT 2007 system, following with the improvements made for our 2008 submission. Then, we discuss in section 5 some post-evaluation experiments made very recently, as well as some on-going work on Arabic / English speech to text translation. This year, the systems were ranked according to the (BLEU+METEOR)/2 score of the primary ASR output run submissions. The LIG was ranked 5th/10 based on this rule.