Conversation Moderator: A Mobile App for Tracking Individual Speaking in Group Conversations

We can naturally observe in group dynamics when some people speak too much, others too little, or when one person consistently speaks over another, however, we often have little objective data to support such assertions. Even when such data can be collected, it is not often processed quickly enough to provide quality feedback to affect behavior. We developed the conversation moderator to provide feedback on the engagement of people speaking in the meeting through a friendly and intuitive user interface. The application utilizes machine learning-based speaker diarization to parse the audio recording into separate individuals, and the results are presented in a variety of visual representations. This includes total speaking time for each person, a scrollable piano chart indicating the time course of each speaker throughout the conversation, and additional statistics on speaking intervals. Diarization was performed with CMU SPHINX for MFCC conversion to extract audio features relevant for speech recognition and the LIUM toolkit to perform the necessary clustering and sequence estimation to distinguish each speaker. This application provides unbiased information on speakers with the goal of facilitating group discussions and aiding in the study of human social interaction.