利用语音情感识别和推荐系统处理治疗聊天机器人中的负面情绪
Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots
AI总结：
本文提出结合语音情感识别（SER）和推荐系统增强治疗聊天机器人，使用CNN和ShEMO数据集识别负面情绪，并通过GloVe与LSTM模型生成个性化情绪管理建议，集成GlowTTS实现英波双语语音交互，显著提升心理健康支持效果。
摘要：
情感健康显著影响心理健康和整体生活质量。随着治疗聊天机器人日益普及，它们理解和共情回应用户情绪的能力仍然有限。本文通过提出一种用听觉感知增强治疗聊天机器人的方法来解决这一局限性，使其能够理解用户感受并提供类人共情。所提方法结合了语音情感识别（SER）技术，使用卷积神经网络（CNN）模型和ShEMO数据集，准确检测和分类愤怒、恐惧和悲伤等负面情绪。SER模型验证准确率达88%，证明了其从语音信号中识别情感状态的有效性。此外，开发了一个推荐系统，利用SER模型的输出生成管理负面情绪的个性化推荐，并为此任务新生成了一个双语数据集，因为目前没有可用数据集。该推荐模型通过结合词表示全局向量（GloVe）和LSTM模型，准确率达到98%。为提供更具沉浸感和共情的用户体验，集成了名为GlowTTS的文本转语音模型，使治疗聊天机器人能够用英语和波斯语向用户口头传达生成的推荐。所提方法具有良好潜力，通过赋予治疗聊天机器人识别和回应用户情绪的能力，最终改善对英语和波斯语用户的心理健康支持服务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
