Automatic Addressee Identification Based on Participants´ Head Orientation and Utterances for Multiparty Conversations

We propose a method that uses the participants´ head orientation and utterances for automatically identifying the addressee of each utterance in face-to-face multiparty conversations, such as meetings. First, each participant´s head orientation is determined through vision-based detection and the presence/absence of utterances is extracted using the power of voices captured by microphones. Second, gaze direction (whom each participant is looking at) is estimated from just detected head orientation using the support vector machine. Third, several related features such as amount and frequency of gaze and eye contact are calculated in each utterance interval. Finally, a Bayesian Network is used to classify each utterance into one of two types of utterances: (a) the speaker is addressing a single participant and (b) the speaker is addressing all participants. Experiments on addressee estimation with 3-person conversations confirm the usefulness of our method