融合音视频与文本分析，精准识别客户情绪，提升客服体验
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis
- 用LSTM/CNN分析音频特征，用DistilBERT处理文本情感
- 在多数据集上实现高精度情绪识别，支持实时处理
- 适合智能客服、用户体验优化等场景
本研究提出一种混合情绪识别系统，融合深度学习、自然语言处理和大语言模型，通过分析语音与文本数据，提升客服中心的客户互动质量。系统结合声学特征与文本情感分析，突破传统方法对复杂情绪理解的局限。采用LSTM与CNN进行音频建模，使用DistilBERT进行文本评估，有效应对语言与文化差异，同时保障实时性。在多个数据集上的严格测试验证了系统的鲁棒性与准确性，展现出显著提升个性化与共情式服务的能力，有助于提高运营效率。该研究为更智能、以人为本的数字通信奠定了基础，推动客户服务标准升级。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。