Exploiting Semantic Relations For A Spoken Language Understanding Application

This article proposes a new confidence measure estimated for concept hypotheses provided by a semantic language model used in the context of a dialog application. This confidence measure is based upon the ontology and more precisely, upon the semantic relations between concepts. It aims at measuring how high a concept hypothesis is related to the other hypotheses of an utterance. The semantic relation confidence measure is evaluated alone, and in combination with a classical acoustic confidence measure. The two measures are also used as parameters of a decision tree. It is shown that the two confidence measures are complementary and yield good performance in terms of cross entropy relative reduction.