Voice Based Stress Analysis and Detection Using Machine Learning
| dc.contributor.author | Darshna V 1NH20CE009; M kumar Venkat 1NH20ME075; Omini rao N 1NH20CE029; Prajwal 1NH20ME085 | |
| dc.date.accessioned | 2025-05-31T09:35:00Z | |
| dc.date.available | 2025-05-31T09:35:00Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/19185 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Voice Based Stress Analysis and Detection Using Machine Learning | |
| dc.type | Learning Object |