PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

AUTOMATIC LEARNING OF INTERPRETATION STRATEGIES FOR SPOKEN DIALOGUE SYSTEMS
Cristian Raymond, Fréderic Béchet, Renato De Mori, Geraldine Damnati and Yannick Esteve
Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing 2004.

Abstract

This 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 con- fidence 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 learn a decision strategy for rescoring or rejecting a 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Speech
ID Code:704
Deposited By:Fréderic Béchet
Deposited On:06 January 2005