Confirming Customer Satisfaction With Tones of Speech

Yen Huei Ko, Ping Yu Hsu, Yu Chin Liu, Po Chiao Yang

研究成果: 雜誌貢獻期刊論文同行評審


As customer satisfaction explicitly leads to repurchase behavior, the level of customer satisfaction affects both sales performance and enterprise growth. Traditionally, measuring satisfaction requires customers spending extra time to fill out a post-purchase questionnaire survey. Recently, ASR (Automatic Speech Recognition) is utilized to extract spoken words from conversation to measure customer satisfaction. However, as oriental people tend to use vague words to express emotion, the approach has its limitation. To solve the problem, this study strived to complete following tasks: devising a process to collect customer voice expressing satisfaction and corresponding verifiable ground truth; a dataset of 150 customer voices speaking in Mandarin was collected; MFCCs were extracted from the voice data as features; as the size of dataset was limited, Auto Encoder was utilized to further reduce the features of voices; models based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) and Support Vector Machine (SVM) were constructed to predict satisfaction. With nested cross validation, the average accuracy of LSTM and SVM could reach 71.95% and 73.97%, respectively.

頁(從 - 到)83236-83248
期刊IEEE Access
出版狀態已出版 - 2022


深入研究「Confirming Customer Satisfaction With Tones of Speech」主題。共同形成了獨特的指紋。