Detection of Depression and Anxiety through Speech, Voice, and Sentiment Analysis

Abstract

This study investigates the potential of using speech, voice, and sentiment analysis for detecting depression and other mental health disorders among 3,995 employees in the IT sector. The study aims to explore the feasibility of using these technologies to detect mental health concerns earlier, to improve diagnosis and treatment outcomes. Participants in the study provided speech and text samples, which were analyzed using various machine-learning algorithms to identify patterns associated with depression and other mental health disorders. The results suggest that speech, voice, and sentiment analysis have the potential to be effective tools for the early detection of mental health concerns among employees in the IT sector.            

 However, ethical and privacy concerns must be addressed before widespread implementation of these technologies. The study highlights the importance of balancing the potential benefits of these technologies with the need to protect individual privacy and ensure the ethical use of sensitive health data. Overall, the study highlights the promise of speech, voice, and sentiment analysis in the field of mental health and the potential for these technologies to improve the lives of individuals in the IT sector and beyond.