Development of a call center customer satisfaction predictor based on speech features - Sait, Alvi Jonathan C.
For a call center company, the customer's perceived quality of a service is one of several determinants of performance. Services that are perceived as good will yield a high level of customer satisfaction (CSAT) and will promote customer's loyalty to the company. However, dissatisfaction arises when the quality of a service is perceived as poor. In order to help call centers gauge the quality of their services, a customer satisfaction evaluation system should be developed. In this project, various prosodic speech features were used in modeling the parameters of a suitable customer satisfaction (CSAT) predictor for practical call centers. Initially, pitch and intensity statistics which best discriminates among emotions were used in training an emotion recognition system that is independent of the context of the words uttered. Four emotions were considered in this study, namely anger, boredom, happiness and neutral. Furthermore, two stages of classification were used for the emotion recognition module. In the first stage, a speech utterance was classified into either the angry/happy (AH) or neutral/bored (NB) group. Next, classification within groups AH and NB was performed to produce the detected emotion of the utterance. The output emotions of the emotion recognition block were then used in training the CSAT predictor model. Support vector machines, k-means clustering and multivariate discriminant analysis were implemented to verify the accuracy of the emotion recognition system and CSAT predictor. The results of the classification algorithms revealed that no single feature can discriminate among the four emotions. Hence, multiple features were used for different levels of classification. Furthermore, the addition of features and corresponding derivatives was able to enhance the performance of the classifiers by creating a more definite distinction among the emotions. With the proper selection of feature combinations, the success rates were improved significantly to an average of 71.80% for the emotion recognition system and 84.81% for the CSAT predictor. As for the emotions, bored was the best recognized emotion while angry remained least recognized by the classifiers.