Abstract
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 data bank of 150 customer voices speaking in Mandarin was collected; the features of the voice data were extracted with MFCC; as the size of data bank 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.
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