Efficient error correction for speech systems using constrained re-recognition

Abstract

Efficient error correction of recognition output is a major barrier in the adoption of speech interfaces. This thesis addresses this problem through a novel correction framework and user interface. The system uses constraints provided by the user to enhance re-recognition, correcting errors with minimal user effort and time. In our web interface, users listen to the recognized utterance, marking incorrect words as they hear them. After they have finished marking errors, they submit the edits back to the speech recognizer where it is merged with previous edits and then converted into a finite state transducer. This FST, modeling the regions of correct and incorrect words in the recognition output, is then composed with the recognizer's language model and the utterance is re-recognized. We explored the use of our error correction technique in both the lecture and restaurant domain, evaluating the types of errors and the correction performance in each domain. With our system, we have found significant improvements over other error correction techniques such as n-best lists, re-speaking or verbal corrections, and retyping in terms of actions per correction step, corrected output rate, and ease of use.