We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-be...
In spoken dialogue systems, it is important for a system to know how likely a speech recognition hypothesis is to be correct, so it can reprompt for fresh input, or, in cases wher...
In a Wizard-of-Oz experiment with multiple wizard subjects, each wizard viewed automated speech recognition (ASR) results for utterances whose interpretation is critical to task s...
Rebecca J. Passonneau, Susan L. Epstein, Tiziana L...
This paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made ...
With the purpose of improving Spoken Language Understanding (SLU) performance, a combination of different acoustic speech recognition (ASR) systems is proposed. State a-posteriori...