AI-Powered Multilingual Mental Health Chatbot with Personalized Voice and Text Support on Streamlit

Year : 2025 | Volume : 03 | Issue : 02 | Page : 40 50
    By

    Sharmila G.,

  • Aiswarya Raj,

  • Prithiga R.,

  • Sasi Aditi G.K.,

  • Shree Nithi C.,

  1. Assistant Professor, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India
  2. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India
  3. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India
  4. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India
  5. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India

Abstract

Mental health concerns are increasingly recognized as one of the most pressing global challenges, with millions of people struggling to access timely, affordable, and personalized support. Barriers such as stigma, lack of professional availability, language differences, and geographical limitations often prevent individuals from seeking help when they need it most. To address this critical gap, this study introduces MindMate, an AI-powered multilingual chatbot specifically designed to provide both text-based and voice-enabled mental health assistance through an easy-to-use Streamlit interface. Built on the Rasa framework, the system incorporates advanced natural language understanding and dialogue management to engage in meaningful, empathetic interactions. MindMate is capable of processing multiple languages by integrating multilingual models, while also including speech recognition for seamless voice input. Furthermore, it employs a personalized recommendation engine that suggests relevant coping strategies, resources, and self-care practices tailored to each user’s needs. By prioritizing inclusivity, accessibility, and emotional sensitivity, MindMate offers a safe, judgment-free space for individuals to share their concerns. Experimental evaluations highlight its efficiency, accuracy, and cultural adaptability, underscoring its potential to transform digital mental health support services worldwide.

Keywords: Mental health chatbot, natural language understanding, Rasa framework, multilingual support, speech recognition, personalized responses, Streamlit

[This article belongs to International Journal of Computer Science Languages ]

How to cite this article:
Sharmila G., Aiswarya Raj, Prithiga R., Sasi Aditi G.K., Shree Nithi C.. AI-Powered Multilingual Mental Health Chatbot with Personalized Voice and Text Support on Streamlit. International Journal of Computer Science Languages. 2025; 03(02):40-50.
How to cite this URL:
Sharmila G., Aiswarya Raj, Prithiga R., Sasi Aditi G.K., Shree Nithi C.. AI-Powered Multilingual Mental Health Chatbot with Personalized Voice and Text Support on Streamlit. International Journal of Computer Science Languages. 2025; 03(02):40-50. Available from: https://journals.stmjournals.com/ijcsl/article=2025/view=232868


References

  1. Nieminen H, Kuosmanen L, Bond R, Vartiainen AK, Mulvenna M, Potts C, Kostenius C. Coproducing multilingual conversational scripts for a mental wellbeing chatbot-where healthcare domain experts become chatbot designers. Eur Psychiatry. 2022 Jun 1; 65(S1): S293–S293.
  2. Potts C, Lindström F, Bond R, Mulvenna M, Booth F, Ennis E, Parding K, Kostenius C, Broderick T, Boyd K, Vartiainen AK. A multilingual digital mental health and well-being Chatbot (ChatPal): pre-post multicenter intervention study. J Med Internet Res. 2023 Jul 6; 25: e43051.
  3. Dinesh DN, Rao MN, Sinha C. Language adaptations of mental health interventions: User interaction comparisons with an AI-enabled conversational agent (Wysa) in English and Spanish. Digit Health. 2024 May; 10: 20552076241255616.
  4. AlMakinah R, Norcini-Pala A, Disney L, Canbaz MA. Enhancing mental health support through human-ai collaboration: Toward secure and empathetic ai-enabled chatbots. In 2025 IEEE Conference on Artificial Intelligence (CAI). 2025 May 5; 196–202.
  5. Mariappan A, Goranci-Buzhala G, Ricci-Vitiani L, Pallini R, Gopalakrishnan J. Trends and challenges in modeling glioma using 3D human brain organoids. Cell Death Differ. 2021 Jan; 28(1): 15–23.
  6. Chin H, Song H, Baek G, Shin M, Jung C, Cha M, Choi J, Cha C. The potential of chatbots for emotional support and promoting mental well-being in different cultures: mixed methods study. J Med Internet Res. 2023 Oct 20; 25: e51712.
  7. Kostenius C, Lindstrom F, Potts C, Pekkari N. Young peoples’ reflections about using a chatbot to promote their mental wellbeing in northern periphery areas-a qualitative study. Int J Circumpolar Health. 2024 Dec 31; 83(1): 2369349.
  8. Potts C, Bond R, Mulvenna MD, Ennis E, Bickerdike A, Coughlan EK, Broderick T, Burns C, McTear MF, Kuosmanen L, Nieminen H. Insights and lessons learned from trialling a mental health chatbot in the wild. In 2021 IEEE Symposium on Computers and Communications (ISCC). 2021 Sep 5; 1–6.
  9. Dhanda A, Goel R, Vashisht S, Susan S. Hindi conversational agents for mental health assistance. Int J Appl Res Inf Technol Comput. 2021; 12(1–3): 12–20.
  10. Barnard RW, Kellogg C. Applications of convolution operators to problems in univalent function theory. Mich Math J. 1980 Jan; 27(1): 81–94.
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 19/03/2025
Accepted 23/04/2025
Published 08/09/2025
Publication Time 173 Days
137

Login


My IP

PlumX Metrics