Multimodal AI Framework Enhances Scrum Master Self-Awareness

A new research paper proposes EGI, a multimodal emotional AI framework designed to improve real-time self-awareness for Scrum Masters and meeting organizers. The system integrates four AI models: speech-to-text for real-time transcription, intonation analysis for prosodic emotional cues, emotion-based vocabulary matching for sentiment in spoken content, and context-aware suggestions via an open-source multi-module AI API. In simulated meeting environments, the automatic speech recognition achieved a word error rate of 10%. Evaluation indicates that real-time feedback significantly enhances emotion awareness during agile meetings. The study addresses a gap in emotion monitoring for Scrum Masters, whose emotional impact on team dynamics is critical. The paper is available on arXiv under identifier 2605.17684.