The Role of Linguistic and Prosodic Cues on the Prediction of Self-Reported Satisfaction in Contact Centre Phone Calls

Call Centre data is typically collected by organizations and corporations in order to ensure the quality of service, supporting for example mining capabilities for monitoring customer satisfaction. In this work, we analyze the significance of various acoustic features extracted from customer-agents’ spoken interaction in predicting self-reported satisfaction by the customer. We also investigate whether speech prosodic features can deliver complementary information to speech transcriptions provided by an ASR. We explore the possibility of using a deep neural architecture to perform early feature fusion on both prosodic and linguistic information. Convolutional Neural Networks are trained on a combination of word embedding and acoustic features for the binary classification task of ”low” and ”high” satisfaction prediction. We conducted our experiments analysing real callcentre interactions of a large corporation in a Spanish spoken country. Our experiments show that linguistic features can predict self-reported satisfaction more accurately than those based on prosodic and conversational descriptors. We also find that dialog turn-level conversational features generally outperforms frame-level signal descriptors. Finally, the fusion of linguistic and prosodic features reports the best performance in our experiments, suggesting the complementarity of the information conveyed by each set of behavioral representation.