Construction of Customer Appeal Classification Model Based on Speech Recognition

Sheng Cao; Yaling Zhang; Shengping Yan; Xiaoxuan Qi; Yuling Li

doi:10.3745/JIPS.04.0272

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자료유형: 학술저널

저자정보: Sheng Cao (State Grid Qinghai Electric Power Company) Yaling Zhang (State Grid Qinghai Electric Power Company) Shengping Yan (State Grid Qinghai Electric Power Company) Xiaoxuan Qi (State Grid Qinghai Electric Power Company) Yuling Li (State Grid Qinghai Electric Power Company)

저널정보: 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제19권 제2호

발행연도: 2023.4

수록면: 258 - 266 (9page)

DOI: 10.3745/JIPS.04.0272

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Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paperproposes a customer classification model based on speech recognition. First, this paper analyzes the temporaldata characteristics of customer demand data, identifies the influencing factors of customer demand behavior,and determines the process of feature extraction of customer voice signals. Then, the emotional associationrules of customer demands are designed, and the classification model of customer demands is constructedthrough cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally,on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognitionis completed. Experimental results show that the proposed method improves the accuracy of the customerdemand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves theproblems of poor customer satisfaction and low customer classification accuracy of the existing classificationmethods, which have practical application value.

#Association Rules #Customer Demands #Euclidean Distance

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