This research presents a hybrid emotion recognition system integrating advanced Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) to analyze audio and textual data for enhancing customer interactions in contact centers. By combining acoustic features with textual sentiment analysis, the system achieves nuanced emotion detection, addressing the limitations of traditional approaches in understanding complex emotional states. Leveraging LSTM and CNN models for audio analysis and DistilBERT for textual evaluation, the methodology accommodates linguistic and cultural variations while ensuring real-time processing. Rigorous testing on diverse datasets demonstrates the system's robustness and accuracy, highlighting its potential to transform customer service by enabling personalized, empathetic interactions and improving operational efficiency. This research establishes a foundation for more intelligent and human-centric digital communication, redefining customer service standards.

翻译：本研究提出了一种混合情感识别系统，该系统集成了先进的深度学习、自然语言处理（NLP）和大语言模型（LLMs），通过分析音频和文本数据来增强联络中心中的客户交互。通过结合声学特征与文本情感分析，该系统实现了细致入微的情感检测，解决了传统方法在理解复杂情感状态方面的局限性。该方法利用LSTM和CNN模型进行音频分析，并采用DistilBERT进行文本评估，在确保实时处理的同时，适应了语言和文化的多样性。在多样化数据集上的严格测试证明了该系统的鲁棒性和准确性，凸显了其通过实现个性化、共情式交互并提升运营效率来变革客户服务的潜力。本研究为构建更智能、更以人为本的数字通信奠定了基础，重新定义了客户服务标准。