Confidence measures for spoken dialogue systems

This paper provides improved confidence assessment for detection of word-level speech recognition errors, out of domain utterances and incorrect concepts in the CU Communicator system. New features from the speech understanding component are proposed for confidence annotation at utterance and concept levels. We have considered a neural network to combine all features in each level. Using the data collected from a live telephony system, it is shown that 53.2% of incorrectly recognized words, 53.2% of out of domain utterances and 50.1% of incorrect concepts are detected at a 5% false rejection rate. In addition, the confidence measures are used to improve the word recognition accuracy. Several hypotheses from different speech recognizers are compiled into a word-graph. The word-graph is searched for the hypothesis with the best confidence. We report a 14.0% relative word error rate reduction after this confidence rescoring.