Speech Emotion Recognition and Sentiment Analysis based Therapist Bot

Ninad Chaudhari

Abstract:

Abstract — 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. Keywords —Speech Emotion Recognition,
Sentiment Analysis, Deep Learning, CNN, BiLSTM, Chatbot