Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
The measurement of user information satisfaction
Communications of the ACM
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Towards developing general models of usability with PARADISE
Natural Language Engineering
Detecting Problematic Dialogs with Automated Agents
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
On NoMatchs, NoInputs and BargeIns: do non-acoustic features support anger detection?
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling user satisfaction with Hidden Markov Model
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
Facing reality: simulating deployment of anger recognition in IVR systems
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
Modeling user satisfaction transitions in dialogues from overall ratings
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Towards quality-adaptive spoken dialogue management
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
Learning to control listening-oriented dialogue using partially observable markov decision processes
ACM Transactions on Speech and Language Processing (TSLP)
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In this work we describe the modeling and prediction of Interaction Quality (IQ) in Spoken Dialogue Systems (SDS) using Support Vector Machines. The model can be employed to estimate the quality of the ongoing interaction at arbitrary points in a spoken human-computer interaction. We show that the use of 52 completely automatic features characterizing the system-user exchange significantly outperforms state-of-the-art approaches. The model is evaluated on publically available data from the CMU Let's Go Bus Information system. It reaches a performance of 61.6% unweighted average recall when discriminating between 5 classes (good to very poor). It can be further shown that incorporating knowledge about the user's emotional state does hardly improve the performance.