Modeling vocal interaction for text-independent detection of involvement hotspots in multi-party meetings

Indexing, retrieval, and summarization in recordings of meetings have, to date, focused largely on the propositional content of what participants say. Although objectively relevant, such content may not be the sole or even the main aim of potential system users. Instead, users may be interested in information bearing on conversation flow. We explore the automatic detection of one example of such information, namely that of hotspots defined in terms of participant involvement. Our proposed system relies exclusively on low-level vocal activity features, and yields a classification accuracy of 84%, representing a 39% reduction of error relative to a baseline which selects the majority class.