On the Use of Confidence for Statistical Decision in Dialogue Strategies
Christian Raymond, Frédéric Béchet, Renato De Mori, Géraldine Damnati · 2004
This paper describes an interpretation and deci-sion strategy that minimizes interpretation er-rors and perform dialogue actions which may not depend on the hypothesized concepts only, but also on confidence of what has been rec-ognized. The concepts introduced here are ap-plied in a system which integrates language and interpretation models into Stochastic Finite State Transducers (SFST). Furthermore, acous-tic, linguistic and semantic confidence mea-sures on the hypothesized word sequences are made available to the dialogue strategy. By evaluating predicates related to these confi-dence measures, a decision tree automatically learn a decision strategy for rescoring a n-best list of candidates representing a user’s utter-ance. The different actions that can be then per-formed are chosen according to the confidence scores given by the tree. 1