Evaluating Temporal Predictive Features for Virtual Patients Feedbacks

Abstract

In the intelligent virtual agent domain, several machine learning models have been proposed to automatically determine the feedbacks of virtual agents during an interaction, using human-human interaction datasets as training corpora and most commonly based on verbal and prosodic features \citeMorency2010, Truong2010a. These approaches suppose an accurate system to automatically recognize speech and prosody. That makes the overall model's performance dependent on the individual performances of speech and prosody recognizers. As a consequence, one challenge remains to identify features that could be easily and accurately recognized during a human-machine interaction for predicting virtual agents' feedbacks in real time.