Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots

Abstract

Emotional well-being significantly influences mental health and overall quality of life. As therapy chatbots become increasingly prevalent, their ability to comprehend and respond empathetically to users' emotions remains limited. This paper addresses this limitation by proposing an approach to enhance therapy chatbots with auditory perception, enabling them to understand users' feelings and provide human-like empathy. The proposed method incorporates speech emotion recognition (SER) techniques using Convolutional Neural Network (CNN) models and the ShEMO dataset to accurately detect and classify negative emotions, including anger, fear, and sadness. The SER model achieves a validation accuracy of 88%, demonstrating its effectiveness in recognizing emotional states from speech signals. Furthermore, a recommender system is developed, leveraging the SER model's output to generate personalized recommendations for managing negative emotions, for which a new bilingual dataset was generated as well since there is no such dataset available for this task. The recommender model achieves an accuracy of 98% by employing a combination of global vectors for word representation (GloVe) and LSTM models. To provide a more immersive and empathetic user experience, a text-to-speech model called GlowTTS is integrated, enabling the therapy chatbot to audibly communicate the generated recommendations to users in both English and Persian. The proposed approach offers promising potential to enhance therapy chatbots by providing them with the ability to recognize and respond to users' emotions, ultimately improving the delivery of mental health support for both English and Persian-speaking users.