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This work presents a practical solution to the problem of call center agentmalpractice. A semi-supervised framework comprising of non-linear powertransformation, neural feature learning and k-means clustering is outlined. Weput these building blocks together and tune the parameters so that the bestperformance was obtained. The data used in the experiments is obtained from ourin-house call center. It is made up of recorded agent-customer conversationswhich have been annotated using a convolutional neural network based segmenter.The methods provided a means of tuning the parameters of the neural network toachieve a desirable result. We show that, using our proposed framework, it ispossible to significantly reduce the malpractice classification error of ak-means-only clustering model which would serve the same purpose. Additionally,by presenting the amount of silence per call as a key performance indicator, weshow that the proposed system has enhanced agents performance at our callcenter since deployment.

A Novel Semi-supervised Framework for Call Center Agent Malpractice Detection via Neural Feature Learning