Case Study: Assessing Depression, Anxiety, and Stress Among 71 Employees Using Manodayam AI Voice Marker Technology
Case Study: Assessing Depression, Anxiety, and Stress Among 71 Employees Using Manodayam AI Voice Marker Technology Objective: A Noida-based education startup aimed to evaluate mental health conditions (depression, anxiety, and stress) among 71 employees using *Manodayam AI Voice Marker Technology*, an AI-powered tool that analyzes vocal patterns to detect emotional and psychological distress. Methodology: Participants: 71 employees (ages 22–45) across various roles. Technology: Manodayam AI analyzed voice recordings for vocal biomarkers such as tone, pitch, speech rate, and pauses. Process: Employees participated in 5–10 minute voice recording sessions, discussing work-related stress, personal experiences, and daily routines. Validation: A subset (n=15) was assessed using DASS-21 (Depression, Anxiety, and Stress Scale) to validate AI findings. Ethics: Informed consent and data anonymity were maintained. Findings: Depression: 18% (13 employees) showed moderate to severe symptoms. Anxiety: 25% (18 employees) exhibited significant anxiety levels. Stress: 30% (21 employees) reported high stress levels. AI results correlated 84% with DASS-21 scores, demonstrating high accuracy Discussion: The study revealed a notable prevalence of mental health issues, with stress being the most common. Manodayam AI proved effective in identifying emotional distress but faced challenges with speech variations in multilingual contexts. Employees expressed concerns about privacy and stigma, which initially hindered participation.
