Speech Emotion AI for Mental Health Monitoring in Call Centers

Computer Science

Mental health issues are increasingly recognized as a global concern, with workplaces and service industries taking greater interest in emotional well-being. Call centers, where agents experience high stress and emotional labor, are environments where mental health monitoring can have transformative benefits. Speech Emotion Recognition (SER) powered by Artificial Intelligence enables real-time analysis of vocal cues to detect emotional states such as stress, frustration, anxiety, or burnout. This paper explores the foundational technologies of speech emotion AI, including acoustic feature extraction, machine learning, and deep neural network architectures. It discusses practical use cases in employee well-being monitoring, burnout prevention, and stress analytics within call centers. Real-world applications and pilot programs are reviewed to assess effectiveness and limitations. Ethical and privacy considerations such as data consent, emotional surveillance, and fairness are critically examined. The paper also addresses technical challenges in emotion generalization, cross-lingual performance, and real-time processing. Finally, it presents future directions such as multimodal emotion detection, explainable SER models, and AI-driven mental health interventions. Speech emotion AI offers a non-intrusive, scalable tool for supporting mental health in high-stress occupational environments like call centers.

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