Chang-mook Oh, HelpU, Inc, South Korea

This paper proposes and develops 'LINCALL,' a real-time voice processing and machine learning system designed to provide preemptive emotional protection for emotional laborers working in environments such as call centers. LINCALL analyzes voice data in real-time conversations with customers to understand their emotional states, converting this data into text for analysis. The system identifies keywords and offers appropriate questions and answers, aiding emotional laborers in handling customer interactions more effectively. It also includes a feature for responding appropriately to the customer's emotional state, such as voice blocking. Additionally, the automatic voice-to-text conversion feature reduces fatigue for emotional laborers, enhances the efficiency of consultations, and aids in preemptive emotional protection for emotional laborers.

Emotional laborers, Emotion analysis, Speech recognition, Pre-emptive emotional protection, Machine learning