11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Is It Possible to Predict Task Completion in Automated Troubleshooters?

Alexander Schmitt (1), Michael Scholz (1), Wolfgang Minker (1), Jackson Liscombe (2), David Suendermann (2)

(1) Universität Ulm, Germany
(2) SpeechCycle Labs, USA

The online prediction of task success in Interactive Voice Response (IVR) systems is a comparatively new field of research. It helps to identify critical calls and enables to react, before it is too late and the caller hangs up. This publication answers, to which extent it is possible to predict task completion and how existing approaches generalize for longer lasting dialogues. We compare the performance of two different modeling techniques: linear modeling and the new n-gram modeling. The study shows that n-gram modeling outperforms linear modeling significantly at later prediction points. From a comprehensive set of interaction parameters we identify the relevant ones using Information Gain Ratio. New interaction parameters are presented and evaluated. The study is based on 41.422 calls from an automated Internet troubleshooter with average turn length of 21.4 turns per call.

Full Paper

Bibliographic reference.  Schmitt, Alexander / Scholz, Michael / Minker, Wolfgang / Liscombe, Jackson / Suendermann, David (2010): "Is it possible to predict task completion in automated troubleshooters?", In INTERSPEECH-2010, 94-97.