计算机与现代化 ›› 2020, Vol. 0 ›› Issue (07): 76-79.

• 人工智能 • 上一篇    下一篇

基于LSTM网络的语音服务质检推荐技术

  

  1. (1.国家电网有限公司客户服务中心,天津300306;2.北京中电普华信息技术有限公司,北京100031)
  • 出版日期:2020-07-06 发布日期:2020-07-15
  • 作者简介:武鹏(1970-),女,陕西兴平人,高级工程师,本科,研究方向:电力营销,客户服务,E-mail: peng-wu1@sgcc.com.cn; 郭晓芸(1987-),女,工程师,硕士,研究方向:电力营销,E-mail: xiaoyun-guo@sgcc.com.cn; 陈鹏(1971-),男,辽宁锦州人,高级工程师,硕士,研究方向:电力营销; 王宗伟(1977-),男,辽宁辽源人,高级工程师,硕士,研究方向:供电服务分析与电力营销稽查; 曹璐(1988-),女,重庆人,本科,研究方向:电力营销,人工智能; 金鹏(1984-),男,江苏徐州人,高级工程师,博士,研究方向:电力大数据。

Voice Service Quality Inspection Based on LSTM Network

  1. (1. State Grid Customer Service Center, Tianjin 300306, China;
    2. Beijing China Power Information Technology Co., Ltd., Beijing 100031, China)
  • Online:2020-07-06 Published:2020-07-15

摘要: 为解决目前95598客服中心语音服务质检效率低、信息处理能力弱的问题,提出一种基于LSTM网络的语音服务质检推荐技术。将传统抽样质检方法所用指标与深度学习相关指标结合,使用LSTM网络充分挖掘各项指标在空间与时间上的深层联系。用有代表性的推荐质检代替随机抽样质检,并结合语音服务中问题语音占比低的特性对算法模型进行改进。实验结果表明,所提出的改进LSTM网络质检推荐模型能够有效提高质检效率和质检针对性。

关键词: 语音服务, 质检, 深度学习, LSTM网络

Abstract: To solve the problem of low quality and low information processing capability of 95598 customer service center voice service quality inspection, a recommendation technology based on LSTM network was proposed. Combining the indicators of traditional sampling methods with indicators related to deep learning methods, the LSTM network was used to explore the inner relationship among various indicators in space and time. Instead of random sampling method, a representative recommendation quality inspection was used and combined with the characteristics of low problem probability in voice service to improve model performance. The experimental results illustrate that the improved model can significantly improve the quality inspection efficiency and directivity.

Key words: voice service, quality inspection, deep learning, LSTM network

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