Online Error Detection of Barge-In Utterances by Using Individual Users' Utterance Histories in Spoken Dialogue System

We develop a method to detect erroneous interpretation results of user utterances by exploiting utterance histories of individual users in spoken dialogue systems that were deployed for the general public and repeatedly utilized. More specifically, we classify barge-in utterances into correctly and erroneously interpreted ones by using features of individual users’ utterance histories such as their barge-in rates and estimated automatic speech recognition (ASR) accuracies. Online detection is enabled by making these features obtainable without any manual annotation or labeling. We experimentally compare classification accuracies for several cases when an ASR confidence measure is used alone or in combination with the features based on the user’s utterance history. The error reduction rate was 15% when the utterance history was used.