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.

提出了一种通过增强语音感知能力来提升治疗聊天机器人对用户情绪理解和人性化回应的方法，该方法使用卷积神经网络（CNN）模型和 ShEMO 数据集的语音情感识别（SER）技术来准确检测和分类负面情绪，包括愤怒、恐惧和悲伤，并使用 SER 模型和 GloVe、LSTM 模型相结合的推荐系统生成个性化管理负面情绪的建议，同时将 GlowTTS 文本到语音模型整合到一起，使治疗聊天机器人能够用英语和波斯语将生成的建议以声音形式传达给用户，最终提高了英语和波斯语用户心理健康支持的交付效果。