Longitudinal Investigation of Work Stressors Using Human Voice Features

Stress is a part of everyone's life. Any event or thought that makes you upset, furious or anxious can set it off. It will affect the human health mentally and physically and produce a negative impact on nervous and immune systems in our body. The human voice carries a lot of information about the person speaking. It also aids in determining a person's current state. In this proposed method, stress was detected using a deep learning model. Automatic stress detection is becoming an intriguing study topic as the necessity for communication between humans and intelligent systems rises. The hormone called cortisol can also be used to determine the body's stress state. For most people, however, it is not a viable option. Speech features are particularly affected by stress, which is combined with the aim that voice data would serve as an easy-to-capture measure of everyday human stress levels and hence as an early warning signal of stress-related health problems. The proposed technique extracts Mel filter bank spectral coefficients from pre-processed voice input and the spectrum coefficients are extracted. The features of Mel frequency cepstral coefficients are applied to feed-forward networks and long short-term memory to predict the status of stress output using a binary decision, i.e., unstressed or stressed. The Mel spectrum and spectrogram output shows the variation in stressed and unstressed voice features. The results of the proposed method indicate better performance compared to an existing model. The model was developed as a web application to be used by workers to test their state of stress at any time.