Adaptive Interview Strategy Based on Interviewees’ Speaking Willingness Recognition for Interview Robots

Social signal recognition techniques based on nonverbal behavioral sensing allow conversational robots to understand the user's social signals, thereby enabling them to adopt interaction strategies based on internal states inferred from the social signals. This research investigates how the online social signal recognition and adaptive dialog strategy influences the dynamic change in a user's inner state. For this purpose, we develop a semiautonomous interview robot system with an online speaker's willingness recognition module and an adaptive question selection module based on the willingness level. The online recognition model of speaker willingness is trained from multimodal nonverbal features extracted using a novel interview corpus, which allows appropriate interview questions to be chosen based on the estimated willingness level of the user. We conduct the experiment using the system to evaluate the effectiveness of adaptive question selection based on the willingness recognition model. First, the multimodal willingness recognition model is evaluated using the interview corpus. The best recognition accuracy of willingness level (high or low) was 72:8% with the random forest classifier. Second, 27 interviewees were interviewed with the two interview robot systems: (I) with the adaptive question selection module based on willingness recognition and (II) with a random question selection strategy. The proposed adaptive question strategy significantly increased the number of utterances with high willingness compared with the baseline system (II); thus, adaptive question selection with online willingness recognition elicited the speaker's willingness even though the model cannot be estimated with near-perfect accuracy.