Voice Based Stress Analysis and Detection Using Machine Learning
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
Artificial intelligence, particularly machine learning, is revolutionizing numerous fields by either supplementing or replacing human efforts, leading to enhanced efficiency and autonomy in systems. Healthcare stands as a notable domain ripe for collaboration with Al and machine learning, offering smoother and more efficient operations. Stress is a pervasive issue in modern society, affecting individuals' mental and physical health. Traditional stress detection methods often rely on self-reporting and physiological measurements, which can be intrusive and impractical for continuous monitoring. The proposed system utilizes vocal biomarkers to identify stress levels in real-time. By analysing various acoustic features of speech, such as pitch, tone, rhythm, and speech rate, the system can detect subtle changes indicative of stress. A comprehensive dataset comprising speech samples from individuals under varying stress conditions was collected and used to train a neural network model. This model was then validated against established stress measurement techniques to ensure its accuracy and reliability. This voice-based approach offers a promising solution for early stress detection and intervention, contributing to better mental health management and improved overall well-being.