Improving the understanding of spoken referring expressions through syntactic-semantic and contextual-phonetic error-correction.

•We present a classifier for detecting Automatic Speech Recognition (ASR) errors.•We offer a mechanism that uses shallow semantic parsing to break up the referring expressions heard by the ASR into labelled semantic segments, which are then used to set up syntactic expectations.•We describe a syntactic-semantic error-correction model that decides how to modify the output of the ASR on the basis of the syntactic expectations of its semantic segments.•We propose a contextual-phonetic model that re-ranks the output of a Spoken Language Understanding (SLU) system on the basis of the phonetic similarity between words mis-heard by the ASR and the contextually-valid candidate interpretations returned by the SLU system.