Confidence Measures for Turkish Call Center Conversations

Robust Optimization and Confidence Interval DEA for Efficiency Evaluation with Intervals Case Study: Evaluating CRM Units in a Call Center in Tehran

Automatic Detection of Anger in Human-Human Call Center Dialogs

Automatic emotion recognition can enhance evaluation of customer satisfaction and detection of customer problems in call centers. For this purpose emotion recognition is defined as binary classification for angry and non-angry on Turkish humanhuman call center conversations. We investigated both acoustic and language models for this task. Support Vector Machines (SVM) resulted in 82.9% accuracy...

Mining Call Center Conversations exhibiting Similar Affective States

Automatic detection and identifying emotions in large call center calls are essential to spot conversations that require further action. Most often statistical models generated using annotated emotional speech are used to design an emotion detection system. But annotation requires substantial amount of human intervention and cost; and may not be available for call center calls because of the in...

Low-cost call type classification for contact center calls using partial transcripts

Call type classification and topic classification for contact center calls using automatically generated transcripts is not yet widely available mainly due to the high cost and low accuracy of call-center grade automatic speech transcription. To address these challenges, we examine if using only partial conversations yields accuracy comparable to using the entire customer-agent conversations. W...

Interaction Mining: the new Frontier of Call Center Analytics

In this paper, we present our solution for pragmatic analysis of call center conversations in order to provide useful insights for enhancing Call Center Analytics to a level that will enable new metrics and key performance indicators (KPIs) beyond the standard approach. These metrics rely on understanding the dynamics of conversations by highlighting the way participants discuss about topics. B...