Multi-Modal Emotion Recognition for Enhanced Requirements Engineering: A Novel Approach
Ben Cheng, Chetan Arora, Xiao Liu, Thuong Hoang, Yi Wang, John Grundy

TL;DR
This paper presents MEmoRE, a multi-modal emotion recognition platform designed to improve requirements engineering by capturing stakeholder emotions through facial, vocal, and textual cues, aiming for more empathetic communication.
Contribution
Introduces a novel multi-modal emotion recognition platform tailored for requirements engineering to enhance stakeholder communication and requirements elicitation.
Findings
Preliminary evaluation shows promising emotion detection accuracy.
Multi-modal approach improves understanding of stakeholder emotions.
Platform design supports integration into various RE stages.
Abstract
Requirements engineering (RE) plays a crucial role in developing software systems by bridging the gap between stakeholders' needs and system specifications. However, effective communication and elicitation of stakeholder requirements can be challenging, as traditional RE methods often overlook emotional cues. This paper introduces a multi-modal emotion recognition platform (MEmoRE) to enhance the requirements engineering process by capturing and analyzing the emotional cues of stakeholders in real-time. MEmoRE leverages state-of-the-art emotion recognition techniques, integrating facial expression, vocal intonation, and textual sentiment analysis to comprehensively understand stakeholder emotions. This multi-modal approach ensures the accurate and timely detection of emotional cues, enabling requirements engineers to tailor their elicitation strategies and improve overall communication…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Engineering Research · Advanced Software Engineering Methodologies
