Research on AI-Based Voice-Based Real-Time Assessment of Psychological Stress

Zhang, Jingyi (Jesy)

European Journal of AI, Computing & Informatics, 2026, vol. 2, issue 1, 76-88

Abstract: This research investigates the application of artificial intelligence (AI) to assess psychological stress in real-time through voice analysis. Addressing the limitations of traditional stress assessment methods, which are often subjective and retrospective, this study explores the potential of AI to provide objective and immediate stress evaluations. The methodology involves collecting voice samples from participants under varying stress conditions, extracting relevant acoustic features, and training machine learning models to classify stress levels. Key acoustic features include pitch, speech rate, intensity, and spectral characteristics. We compare the performance of different AI models, including Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs), in accurately detecting stress. The experimental results demonstrate the feasibility and reliability of using AI-based voice analysis for real-time stress assessment. The proposed system offers significant advantages in various applications, such as mental health monitoring, workplace stress management, and emergency response scenarios. The findings highlight the potential of AI technology to transform the way stress is measured and managed, paving the way for more proactive and personalized interventions. Future work will focus on improving the robustness and generalizability of the models across diverse populations and environments.

Keywords: Psychological Stress; Voice Analysis; Artificial Intelligence; Machine Learning; Real-Time Assessment; Acoustic Features; Stress Detection (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:2:y:2026:i:1:p:76-88

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