A Multilingual AI-Powered Mental Health Chatbot Using Gemini API with Conversational Memory and Voice Interaction

Authors

  • Tanuja M J PG Scholar, Department of CSE, GSSS Institute of Engineering and Technology for Women, Karnataka, India. Author
  • Rajashekar M B2 Associate professor, Department of CSE, GSSS Institute of Engineering and Technology for Women, Karnataka, India. Author

DOI:

https://doi.org/10.47392/10.47392/IRJAEH.2026.0641

Keywords:

Artificial Intelligence, Mental Health Chat bot, Gemini API, Multilingual System, Voice Recognition, Natural Language Processing, Emotional Support System, Human-Computer Interaction, Conversational AI, Generative AI, Speech-to-Text Systems

Abstract

Mental health challenges have become increasingly common among students, working professionals, and individuals facing academic pressure, workplace stress, emotional instability, and social isolation [1], [2]. Due to social stigma, limited access to professional counseling, high consultation costs, and language barriers, many people hesitate to seek timely psychological support [3]. To overcome this problem an AI-powered multilingual mental health chatbot designed to provide immediate emotional assistance through natural and supportive conversation. The paper proposes using Google Gemini API integrated with a Flask-based backend and a responsive web interface, enabling intelligent human-like interactions in English, Kannada, and Hindi. To improve accessibility and user engagement, the chatbot incorporates voice input functionality using browser-based speech recognition, allowing users to communicate through speech in addition to text [4]. The system also maintains conversation history to preserve context and continuity. Unlike traditional rule based chatbots, the proposed model leverages generative artificial intelligence to deliver context-aware, empathetic, and language consistent responses without providing medical diagnosis [5], [6]. Experimental evaluation based on simulated user interactions demonstrates improved response relevance, multilingual consistency, and user satisfaction, with strong performance across standard evaluation metrics such as accuracy, precision, recall, and F1-score [7].

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Published

2026-07-24

How to Cite

A Multilingual AI-Powered Mental Health Chatbot Using Gemini API with Conversational Memory and Voice Interaction . (2026). International Research Journal on Advanced Engineering Hub (IRJAEH), 4(07), 4879-4884. https://doi.org/10.47392/10.47392/IRJAEH.2026.0641