A Multilingual Mental Health Support Chatbot with Regional Language Capabilities Based On the AMER Model

Despite that, access to mental health care is still limited in many areas, particularly where there are differences in language and cultural considerations prove challenging. Many available solutions poorly respond to the needs for inclusive and personalized support in local languages. We present the development of our advanced multilingual mental health support chatbot, AMER: Advanced Multilingual Emotional Response Model with Regional Language Capabilities. Using sophisticated NLP, emotion detection, and speech technologies, the chatbot aims to provide empathetic, relevant, and interactive support for the users in their desired languages. This model is responsible for the emotion prediction and classification; AMER runs through six advanced algorithms: sentiment analysis, emotion detection, multilingual support, emoji recognition, personalized recommendation, and speech emotion recognition. To make sure that it recognizes emotions and processes language accurately, the system is trained on datasets such as GoEmotions, Common Voice, and IndicCorp to accommodate ten different languages, including as many local languages (especially in Indian context). Thanks to its modular design, it can process user inputs, evaluate emotions, and produce tailored, context-sensitive outputs. Emotion detection accuracy at baseline testing was 87%, while language processing accuracy was greater than 92%, boosting user engagement by 76% in relation to standard text-based systems. Future improvements will center around multimodal features like expression and tone analysis, as well as integration with teletherapy platforms. The improvements follow the goal of making the chatbot more accessible and better performing for more users, specifically targeting underserved communities worldwide to broaden the accessibility of mental health support.