Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings
This work addresses meeting analysis for researchers and practitioners, but it is incremental as it builds on prior studies by integrating multiple feature types in a formal machine learning setting.
The paper tackled the problem of detecting involvement hot spots in meetings by investigating acoustic, linguistic, and pragmatic features, finding that the lexical model was most informative with incremental contributions from interaction and acoustic-prosodic components.
Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years. These are regions of meetings that are marked by high participant involvement, as judged by human annotators. However, prior work was either not conducted in a formal machine learning setting, or focused on only a subset of possible meeting features or downstream applications (such as summarization). In this paper we investigate to what extent various acoustic, linguistic and pragmatic aspects of the meetings, both in isolation and jointly, can help detect hot spots. In this context, the openSMILE toolkit is to used to extract features based on acoustic-prosodic cues, BERT word embeddings are used for encoding the lexical content, and a variety of statistics based on speech activity are used to describe the verbal interaction among participants. In experiments on the annotated ICSI meeting corpus, we find that the lexical model is the most informative, with incremental contributions from interaction and acoustic-prosodic model components.