Abstract

Floor control is a scheme used by people to organize speaking turns in multi-party conversations. Identifying the floor control shifts is important for understanding a conversation’s structure and would be helpful for more natural human computer interaction systems. Although people tend to use verbal and nonverbal cues for managing floor control shifts, only audio cues, e.g., lexical and prosodic cues, have been used in most previous investigations on speaking turn prediction. In this paper, we present a statistical model to automatically detect floor control shifts using both verbal and nonverbal cues. Our experimental results show that using a combination of verbal and nonverbal cues provides more accurate detection.

Citations