Towards the Automatic Detection of Involvement in Conversation

Although an increasing amount of research has been carried out into human-machine interaction in the last century, even today we are not able to fully understand the dynamic changes in human interaction. Only when we achieve this, will we be able to go beyond a one-to-one mapping between text and speech and be able to add social information to speech technologies. Social information is expressed to a high degree through prosodic cues and movement of the body and the face. The aim of this paper is to use those cues to make one aspect of social information more tangible; namely participants’ degree of involvement in a conversation. Our results for voice span and intensity, and our preliminary results on the movement of the body and face suggest that these cues are reliable cues for the detection of distinct levels of participants involvement in conversation. This will allow for the development of a statistical model which is able to classify these stages of involvement. Our data indicate that involvement may be a scalar phenomenon.