Detecting Group Turn Patterns in Conversations Using Audio-Video Change Scale-Space

Automatic analysis of conversations is important for extracting high-level descriptions of meetings. In this work, as an alternative to linguistic approaches, we develop a novel, purely bottom-up representation, constructed from both audio and video signals that help us characterize and build a rich description of the content at multiple temporal scales. We consider the evolution of the detected change, using Bayesian Information Criterion (BIC) at multiple temporal scales to build an audio-visual change scale space. Peaks detected in this representation, yields group-turn based conversational changes at different temporal scales. Conversation overlaps, changes and their inferred models offer an intermediate-level description of meeting videos that can be useful in summarization and indexing of meetings. Results on NIST meeting room dataset showed a true positive rate of 88%.