This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machinelearning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25. 70-28.99% to 15.72%. *
REF identify user corrections of the system's recognition errors from speech prosody, ASR confidence scores, and the dialogue history.
Characterizing and Predicting Corrections in Spoken Dialogue Systems
Computational Linguistics
Computational Linguistics
Computer Science