Mental Health Monitoring Through Voice Analysis

Abstract

Mental Although mental health problems are becoming more common worldwide, many people suffer great difficulties getting appropriate treatment because of stigma, lack of resources, and low knowledge. By means of speech data, the system offers real-time evaluations of mental health, therefore producing emotion-based feedback to support early intervention.

Introduction

Context:
Mental health issues such as stress, anxiety, and depression affect millions globally. In India, rising educational costs exacerbate financial and emotional stress for students. Access to mental health professionals is limited, creating a need for accessible monitoring tools.

Proposed Solution:
An AI-powered system using voice analysis to detect emotional states (happiness, sadness, anger) from speech patterns like pitch, tone, and pace. The system employs advanced machine learning models, including BERT, to analyze voice data in multiple languages, making it accessible to diverse populations. It provides real-time visualizations of emotional trends to help individuals and healthcare providers track mental well-being over time.

Key Features:

• Emotion Detection: Uses speech recognition and deep learning to identify emotional states and levels of depression.

• Multilingual Support: Capable of analyzing multiple languages with automatic translation where needed.

• Interactive Visualizations: Displays emotional trends over time for self-awareness and proactive mental health management.

• Personalized Recommendations: Offers tailored mental health resources like exercises and music based on detected emotions.

• Emergency Alerts: Notifies designated contacts if signs of critical emotional distress are detected.

• User Management: Secure login and profile management ensure personalized and protected access.

• Historical Data: Tracks and stores past emotional data for trend analysis and insight.

Technical Approach:

• Speech Recognition: Converts spoken words to text using Google Speech API.

• Emotion Analysis: BERT-based deep learning models analyze text to predict emotional states.

• Data Handling: Uses tokenization, probability scoring, and emotion refinement for accurate emotion mapping.

• Visualization: Employs interactive tools like Plotly for clear presentation of emotional data.

• Robustness: Includes error handling to manage speech recognition failures and ensure reliability.

Objectives:

• Provide a non-intrusive, accessible mental health monitoring tool.

• Facilitate early detection of mental health issues.

• Reduce stigma associated with seeking mental health support.

• Empower users to manage their emotional well-being proactively.

Implementation:
Built on a three-layer architecture (frontend, backend, database) for scalability, performance, and security, delivered via a user-friendly web interface (Streamlit).

Impact:
This system offers a scalable, cost-effective, and inclusive solution to monitor mental health through voice, enabling timely interventions and better emotional self-management, especially where access to psychiatrists is limited.

Conclusion

AI-powered voice analysis offers considerable potential for changing the face of monitoring mental health through non-intrusive, practical, and efficient detection of emotional states. Thus, the system would empower individuals to track their mental well-being and enable early intervention and proactive care.As this technology evolves, it has the potential to break down barriers in mental health care and offer a scalable solution that can be used worldwide.

References

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Copyright

Copyright © 2025 Prof. R Sahaya Shamini, Prajna Dsouza, Pratiksha Niranjana Karkera, Swathi , Swathi Bhat S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.