Voice Service Quality Inspection Based on LSTM Network

Abstract

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

CLC Number:

TM-9
F274

WU Peng, GUO Xiao-yun, CHEN Peng, WANG Zong-wei, CAO Lu, JIN Peng. Voice Service Quality Inspection Based on LSTM Network[J]. Computer and Modernization, 2020, 0(07): 76-79.

References

［1］王大伟. 基于语音分析技术的电力客户服务质量检测与分析探究［J］. 电子测试, 2014(3):100-101.
［2］李如雄. 基于语音分析的智能质检系统设计［J］. 自动化与仪器仪表, 2017(6):114-116.
［3］戴东良. 面向呼叫中心话音服务质量监控的关键词检出技术研究与应用［D］. 杭州:浙江工商大学, 2012.
［4］林可希. 智能语音技术在热线服务质检中的应用探讨［J］. 无线互联科技, 2013(12):142-144.
［5］罗旭. 智能语音分析系统在呼叫中心的推广与应用［J］. 课程教育研究, 2013(11):10.
［6］王攀,刘世栋. 电力IMS网络话音质量测量方法与评估模型［J］. 计算机与现代化, 2017(3):40-45.
［7］ CHOI S. Organizational knowledge and information technology: The key resources for improving customer service in call centers［J］. Information Systems and e-Business Management, 2018,16(1):187-203.
［8］ KORFIATIS N, STAMOLAMPROS P, KOUROUTHANASSIS P, et al. Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews［J］. Expert Systems with Applications, 2019,116:472-486.
［9］ LIU J, WANG C M, ZHANG Y N, et al. Automatic satisfaction analysis in call centers considering global features of emotion and duration［J］. Journal of Beijing Institute of Technology, 2018,27(1):58-64.
［10］姜冬. 基于客户体验的95598新型服务模式［J］. 电气技术, 2018,19(9):111-114.
［11］MERITY S, KESKAR N S, SOCHER R. Regularizing and optimizing LSTM language models［J］. Computation and Language, 2017:arXiv preprint arXiv:1708.02182.
［12］贾智慧. 客服中心语音质检系统设计与实现［D］. 西安:西安电子科技大学, 2015.
［13］SUNG Y C, HO Y S, LIN Y B, et al. Voice/video quality measurement for LTE services［J］. IEEE Wireless Communications, 2018,25(4):96-103.
［14］杨基明,孙恒超. 智能语音技术在客服系统中的应用［J］. 电信快报(网络与通信), 2015(3):20-22.
［15］高永梅,王涛. 客户服务系统在电力企业中的应用［J］. 计算机与现代化, 2006(12):142-144.
［16］祝嘉声. 基于DNN的汉语语音识别声学模型的研究［D］. 哈尔滨:哈尔滨工业大学, 2014.
［17］唐贤伦,杜一铭,刘雨微,等. 基于条件深度卷积生成对抗网络的图像识别方法［J］. 自动化学报, 2018,44(5):855-864.
［18］郭丽丽,丁世飞. 深度学习研究进展［J］. 计算机科学, 2015,42(5):28-33.
［19］沈然,林恺丰,吴慧. 一种用于供电服务评估的多模态多任务框架［J］. 计算机与现代化, 2018(12):55-59.
［20］郝岩,白艳萍,张校非. 基于SAR目标识别的深度学习方法［J］. 火力与指挥控制, 2019,44(10):22-26.
［21］叶硕,杜珍珍,彭春堂,等. 基于HMM的混响环境下语音识别研究［J］. 计算机技术与发展, 2019(8):76-80.
［22］刘颖. 金融业客服呼叫中心质量管理创新的案例研究［D］. 杭州:浙江大学, 2018.
［23］何炎祥,孙松涛,牛菲菲,等. 用于微博情感分析的一种情感语义增强的深度学习模型［J］. 计算机学报, 2017,40(4):773-790.
［24］DALY A, DEKKER T, HESS S. Dummy coding vs effects coding for categorical variables: Clarifications and extensions［J］. Journal of Choice Modelling, 2016,21:36-41.
［25］YUAN Z H, LI J, LI Z, et al. Softmax regression design for stochastic computing based deep convolutional neural networks［C］// Proceedings of the on Great Lakes Symposium on VLSI 2017. ACM, 2017:467-470.
［26］刘建,赵加奎,杨维,等. 电力95598客户服务质检抽样算法研究［J］. 电网技术, 2015,39(11):3163-3168.
［27］关浩华. 基于语音分析的智能质检关键词提取方法设计［J］. 自动化与仪器仪表, 2017(7):106-108.
［28］GUNAWARDANA A, SHANI G. A survey of accuracy evaluation metrics of recommendation tasks［J］. Journal of Machine Learning Research, 2009,10(12):2935-2962.

[1]	QI Xian, LIU Daming, CHANG Jiaxin. Multi-view 3D Reconstruction Based on Improved Self-attention Mechanism [J]. Computer and Modernization, 2024, 0(11): 106-112.
[2]	CHEN Kai1, LI Yiting1, 2, QUAN Huafeng1. A River Discarded Bottles Detection Method Based on Improved YOLOv8 [J]. Computer and Modernization, 2024, 0(11): 113-120.
[3]	YANG Jun1, HU Wei1, ZHU Wenfu2. Visual SLAM Loop Closure Detection Algorithm Based on Improved MobileNetV3 [J]. Computer and Modernization, 2024, 0(10): 21-26.
[4]	WANG Yingying, HAO Xiao. Fine-grained Image Classification Based on Res2Net and Recursive Gated Convolution [J]. Computer and Modernization, 2024, 0(10): 74-79.
[5]	SHI Xingyu1, LI Qiang2, ZHUANG Li3, LIANG Yi3, WANG Qiulin3, CHEN Kai3, WU Chenzhou3, CHANG Sheng1. Object Detection Models Distillation Technique for Industrial Deployment [J]. Computer and Modernization, 2024, 0(10): 93-99.
[6]	ZHANG Ze1, ZHANG Jianquan2, 3, ZHOU Guopeng2, 3. Camera Module Defect Detection Based on Improved YOLOv8s [J]. Computer and Modernization, 2024, 0(09): 107-113.
[7]	CHENG Yazi1, LEI Liang1, 2, CHEN Han1, ZHAO Yiran1. Multi-scale Depth Fusion Monocular Depth Estimation Based on Transposed Attention [J]. Computer and Modernization, 2024, 0(09): 121-126.
[8]	CHENG Meng, LI Hao. Improved Deciduous Tree Nest Detection Method Based on YOLOv5s [J]. Computer and Modernization, 2024, 0(08): 24-29.
[9]	WANG Mengxi, LI Jun. Review of Fall Detection Technologies for Elderly [J]. Computer and Modernization, 2024, 0(08): 30-36.
[10]	SHI Xianwei1, FAN Xin2. Semantic Segmentation of Video Frame Scene Based on Lightweight [J]. Computer and Modernization, 2024, 0(08): 49-53.
[11]	XU Xin’ai, LI Gang. An Image Generation Method of Classroom Expression Images [J]. Computer and Modernization, 2024, 0(08): 88-91.
[12]	GAO Shuaipeng, WANG Yifan. Survey on Group-level Emotion Recognition in Images [J]. Computer and Modernization, 2024, 0(08): 98-107.
[13]	HUANG Wendong, WANG Yifan. Survey on Multimodal Information Processing and Fusion Based on Modal Categories [J]. Computer and Modernization, 2024, 0(07): 47-62.
[14]	WU Li1, ZHANG Zhenghao2, GE Caicheng2, YU Jun2. Lane Line Detection Algorithm Based on Improved SCNN Network [J]. Computer and Modernization, 2024, 0(07): 87-92.
[15]	ZHANG Ke1, AI Zhongliang2, LIU Zhonglin3, GU Pingli1, LIU Xuelin4. Judicial Argumentation Understanding Method Based on Multiplet Loss [J]. Computer and Modernization, 2024, 0(06): 115-120.