EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness

TL;DR

This paper introduces EGI, a multimodal AI framework that monitors and enhances Scrum Masters' emotional awareness during meetings through real-time emotion detection and feedback.

Contribution

The paper presents a novel multimodal AI system integrating speech transcription, prosody analysis, sentiment detection, and context-aware suggestions for Scrum Masters.

Findings

Achieved 10% WER in simulated environments.

Real-time feedback improves emotion awareness.

Helps Scrum Masters identify and reduce negative emotions.

Abstract

While increasing research focuses on the emotional well-being of agile team members, a significant gap remains in emotion monitoring studies for Scrum Masters and meeting organizers, whose impact on team dynamics is crucial. This paper proposes a novel application integrating four carefully selected and recommended AI models to monitor the unconsciously expressed emotions of these key roles. This is achieved through: real- time transcription using a speech-to-text model; thresholding for intonation analysis to detect emotional cues in prosody; applying emotion-based vocabulary matching to identify sentiment in spoken content; and providing context-aware suggestions containing emotion keywords using an open-source, multi-module AI API. The system achieved an ASR word error rate WER of 10% in simulated meeting environments. Our evaluation shows that real- time feedback significantly improves emotion awareness during simulated agile meetings, providing Scrum Masters and meeting organizers with real-time and practical suggestions to help them quickly identify and minimize the expression of negative emotions, fostering more positive and effective team interactions.