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By Frédéric Béchet and Benoit Favre


Even though small ASR errors might not impact downstream processes that make use of the transcript, larger error segments like those generated by OOVs can have a considerable impact on applications such as speech-to-speech translation and can eventually lead to communication failure between users of the system. This work focuses on error detection in ASR output targeted towards significant error segments that can be recovered using a dialog system. We propose a CRF system trained to recognize error segments with ASR confidence-based, lexical and syntactic features. The most significant error segment is passed to a dialog system for interactive recovery in which rephrased words are reinserted in the original. 22 % of utterances can be fully recovered and an interesting by-product is that rewriting error segments as a single token reduces WER by 17 % on an adverse corpus

Topics: Index Terms — Automatic Speech Recognition, Confidence Measure, Error Detection, Speech to Speech translation
Year: 2014
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