基于LSTM网络的语音服务质检推荐技术

Voice Service Quality Inspection Based on LSTM Network

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。

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.