COMPUTATIONAL MODELING OF TURN-TAKING DYNAMICS IN SPOKEN CONVERSATIONS
Shammur Absar Chowdhury · Unitn-eprints PhD (University of Trento) · 2017
4. study the role of speakers and context (i.e., agents' and customers' speech) for conveying the information of competitiveness for each individual feature set and their combinations.5. investigate the function of long silences towards the information flow in a dyadic conversation.The extracted turn-taking cues are then used to automatically predict the outcome of the conversation, which is modeled from continuous manifestations of emotion.The contributions include 1. modeling the state of the observed user satisfaction in terms of the final emotional manifestation of the customer (i.e., user).2. analysis and modeling turn-taking properties to display how each turn type influence the user satisfaction.3. study of how turn-taking behavior changes within each emotional state.Based on the studies conducted in this work, it is demonstrated that turn-taking behavior, specially competitiveness of overlaps, is more than just an organizational tool in daily human interactions.It represents the beneficial information and contains the power to predict the outcome of the conversation in terms of satisfaction vs not-satisfaction.Combining the turn-taking behavior and the outcome of the conversation, the final and resultant goal is to design a conversational profile for each speaker.Such profiled information not only facilitate domain experts but also would be useful to the call center agent in real time.These systems are fully automated and no human intervention is required.The findings are potentially relevant to the research of overlapping speech and automatic analysis of human-human and human-machine interactions.At the same time, the work opens up a new perspective on functions of silence towards information flow.