diaLogic: Non-Invasive Speaker-Focused Data Acquisition for Team Behavior Modeling

TL;DR

diaLogic is a non-invasive, human-in-the-loop system that models team behavior during problem solving by analyzing voice data features, providing automated data collection and hypothesis extraction.

Contribution

The paper introduces diaLogic, a novel system for non-invasive, automated team behavior modeling using voice data features within an intuitive GUI.

Findings

Effective modeling of team behavior through voice features.

Automated data acquisition with high performance.

Versatile application across different team problem-solving scenarios.

Abstract

This paper presents diaLogic system, a Human-In-A-Loop system for modeling the behavior of teams during solving open-ended problems. Team behavior is modeled through the hypotheses extracted from features computed from acquired voice data. These features include speaker interactions, speaker emotions, fundamental frequencies, and the corresponding text and clauses. Hypotheses about the invariant and differentiated situations are found based on the similarities and dissimilarities of the behavior of teams over time. To provide full automation of data acquisition, the diaLogic system is executed within an intuitive, user-friendly GUI interface. Experiments present the performance of the system for a broad set of cases featuring team behavior during problem solving.