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Speech Emotion Recognition and Sentiment Analysis based Therapist Bot

Access to mental health care and treatment is a global concern. The demand for these services outnumbers the supply. Online treatment delivered by a chatbot might not only provide access to cost-effective aid, but it could also be convenient for individuals, who are hesitant to participate in therapy. The main aim is to leverage technology to enable more people to seek help. The proposed method has built a responsive therapist bot that generates appropriate responses according to a person's emotion. The detection phase uses an ensemble of deep learning models for Speech Emotion Recognition (SER) and text based sentiment analysis, which classifies one's emotions into four categories – happy, sad, angry, and anxious. The proposed approach can be proved to be fool-proof and it is better than existing methods due to the ensemble of two efficient models CNN (83%) and BiLSTM (92%). In addition to detection and responses, this application recommends suitable tasks to assist the target audience.