Automatic learning of interpretation strategies for spoken dialogue systems

The paper proposes a new application of automatically trained decision trees to derive the interpretation of a spoken sentence. A new strategy for building structured cohorts of candidates is also described. By evaluating predicates related to the acoustic confidence of the words expressing a concept, the linguistic and semantic consistency of candidates in the cohort and the rank of a candidate within a cohort, the decision tree automatically learns a decision strategy for rescoring or rejecting an n-best list of candidates representing a user´s utterance. A relative reduction of 18.6% in the understanding error rate is obtained by our rescoring strategy with no utterance rejection and a relative reduction of 43.1% of the same error rate is achieve with a rejection rate of only 8% of the utterances.