Domain adaptation for parsing in automatic speech recognition

This paper addresses the problem of adapting a parser trained on out-of-domain data for use in automatic speech recognition (ASR) rescoring and error detection tasks. Using a self-training approach and adaptation with weakly-supervised data, we obtain improvements in ASR rescoring of confusion networks. Features extracted from the parser output are also used to improve detection of general ASR errors and out-of-vocabulary word regions in conjunction with a maximum entropy classifier.