9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

When Calls Go Wrong: How to Detect Problematic Calls Based on Log-Files and Emotions?

Ota Herm (1), Alexander Schmitt (2), Jackson Liscombe (3)

(1) Czech Technical University in Prague, Czech Republic; (2) University of Ulm, Germany; (3) SpeechCycle Inc., USA

Traditionally, the prediction of problematic calls in Interactive Voice Response systems in call centers has been based either on dialog state transitions and recognition log data, or on caller emotion. We present a combined model incorporating both types of feature sets that achieved 79.22% classification accuracy of problematic and non-problematic calls after only the first four turns in a human-computer dialogue. We found that using acoustic features to indicate caller emotion did not yield any significant increase of accuracy.

Full Paper

Bibliographic reference.  Herm, Ota / Schmitt, Alexander / Liscombe, Jackson (2008): "When calls go wrong: how to detect problematic calls based on log-files and emotions?", In INTERSPEECH-2008, 463-466.