Any customer initiated inbound call is not only essential for the service agent to respond, but also to resolve the problem. Although agents are trained not to be expressive and provide solution timely and in best possible ways, but the course of the conversation actually plays an important role in deciding the outcome of the call, whether it ends with a positive or negative note. Timely identifying dissatisfied customer based on the automatic analysis of the conversation to measure the positivity of a call, can aid to improve customer satisfaction index and the call handling capabilities of the service desk. In this paper, to automatically analyze the call conversation, we propose a system that extract non-linguistic features, create patterns using multi-dimensional representations, followed by a dynamic algorithm to find the similarity measures. Further, we fuse the information regarding the trend in the variation of the affective content throughout the conversation to get a final score that quantifies the positiveness in a call. Finally, ranking contact center calls in the decreasing order of the positivity measure and evaluate the system using a ranking agreement metric.
Cite as: Pandharipande, M., Tiwari, U., Chakraborty, R., Kopparapu, S.K. (2020) Ranking Contact Center Conversations using Dynamic Programming based Pattern Matching. Proc. Workshop on Speech, Music and Mind (SMM 2020), 16-20, doi: 10.21437/SMM.2020-4
@inproceedings{pandharipande20_smm,
author={Meghna Pandharipande and Upasana Tiwari and Rupayan Chakraborty and Sunil Kumar Kopparapu},
title={{Ranking Contact Center Conversations using Dynamic Programming based Pattern Matching}},
year=2020,
booktitle={Proc. Workshop on Speech, Music and Mind (SMM 2020)},
pages={16--20},
doi={10.21437/SMM.2020-4}
}