MUESLI: multiple utterance error correction for a spoken language interface

We propose a method for using all available information to help correct recognition errors in tasks that use constrained grammars of the kind used in the domain of Command and Control (CC) systems. In current spoken language CC systems, if there is a recognition error, the user repeats the same phrase multiple times until a correct recognition is achieved. This interaction can be frustrating for the user, especially at high levels of ambient noise. We aim to improve the accuracy of the error correction process by using all the previous information available at a given point, this being the previous utterances of the same input phrase and the knowledge that the previous result contained an error.