Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings

TL;DR

This paper explores how acoustic, linguistic, and pragmatic features can be combined using machine learning to identify hot spots of participant involvement in meetings, advancing meeting analysis techniques.

Contribution

It introduces a comprehensive approach integrating acoustic, lexical, and interaction features for hot spot detection in meetings within a formal machine learning framework.

Findings

Lexical features are most informative for hot spot detection.

Combining multiple feature types improves detection accuracy.

Acoustic and interaction features provide incremental benefits.

Abstract

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.