The Correction of Ill-Formed Input Using History-Based Expectation with Applications to Speech Understanding

Abstract

A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs. Error correction is done by strongly biasing parsing toward expected meanings unless clear evidence from the input shows the current sentence is not expected. A dialogue acquisition and tracking algorithm is presented along with a description of its implementation in a voice interactive system. A series of tests are described that show the power of the error correction methodology when stereotypic dialogue occurs.