Semantic confidence measurement for spoken dialog systems

Official URL: http://dx.doi.org/10.1109/TSA.2005.848879

This paper proposes two methods to incorporate semantic information into word and concept level confidence measurement. The first method uses tag and extension probabilities obtained from a statistical classer and parser. The second method uses a maximum entropy based semantic structured language model to assign probabilities to each word. Incorporation of semantic features into a lattice posterior probability based confidence measure provides significant improvements compared to posterior probability when used together in an air travel reservation task. At 5% False Alarm (FA) rate relative improvements of 28% and 61% in Correct Acceptance (CA) rate are achieved for word level and concept level confidence measurements, respectively.