运营商智能客服的关键技术和发展趋势

doi:10.11959/j.issn.1000-0801.2023110

Abstract

Abstract:

The technology development trend of intelligent customer service for operators was discussed, and automatic speech recognition (ASR) transcription error correction technology, semantic extraction technology, encrypted database, and agent access control technology were introduced.Two different research directions of error detection and automatic error correction for speech recognition were analyzed, two directions of supervised learning and unsupervised learning were discussed, and big data encryption and agent access control technologies were introduced.At the same time, the technical directions of multimodal interaction technology, intelligent recommendation technology and disabled-oriented services were prospected.In conclusion, the development and innovation of intelligent customer service technology will bring a more efficient and convenient service experience to the communication industry and strongly promote the progress of the industry service level.

Key words: AI customer service, key technology, AI

CLC Number:

TP393

Xiaoliang MA, Ying LIU, Dequan DU, Lingling AN. Key technologies and development trends of intelligent customer service for operators[J]. Telecommunications Science, 2023, 39(5): 76-89.

Figures/Tables 9

References 65

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研究方向	目标	技术手段	实现方式	局限性
语音识别的错误检测	减少误识别率，提高识别准确率	基于统计学方法和机器学习算法等来检测是否存在错误	可以作为一项独立的任务进行	只能检测出可能存在的错误，不能进行自动纠错
语音识别的自动纠错	提高识别准确率，提高用户体验	基于声学模型、语言模型和上下文信息等来纠正错误	可以在语音识别系统中嵌入实现	需要大量语音数据训练，同时模型的准确度受限于语音数据的质量和覆盖范围

研究方向	目标	技术手段	实现方式	局限性
无监督的语义提取	自动获取高维数据的特征，用于聚类、分类等任务	基于无监督深度学习算法，如自编码器、VAE等	可以独立完成，不需要大量标注数据	无法保证提取到的特征与任务相关，效果可能不如有监督方法好
有监督语义提取	在标注数据上进行训练，从而实现对新数据的分类、识别等任务	基于监督深度学习算法，如 CNN、RNN、Transformer等	需要大量的标注数据来进行训练	需要大量标注数据，同时模型容易过拟合，泛化能力有限

研究方向	目标	技术手段	实现方式	局限性
大数据加密	在维护数据隐私的同时，尽可能地减少数据处理的效率损失	基于加密算法、混淆技术、数据分散等技术实现数据保护	在数据处理过程中进行加密和保护	可能会降低大数据分析和处理的效率，需要权衡数据安全和处理效率的平衡
坐席访问控制	保障坐席的安全性和稳定性，以及客户信息的保密性	使用身份验证、权限管理、访问控制等技术控制坐席访问	在坐席使用和管理过程中进行访问控制和权限设置	需要正确定义角色和权限管理机制，避免权限过松或过严而影响工作效率

Key technologies and development trends of intelligent customer service for operators

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