Towards Adaptive Spoken Dialog Systems, 2013

In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and  accurate use. 

Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted  recordings several thousand real users from commercial and non-commercial SDS.

Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and  inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.

Introduction.- Background and Related Research.- Interaction Modeling and Platform Development.- Novel Strategies for Emotion Recognition.- Novel Approaches to Pattern-based Interaction Quality Modeling.- Statistically Modeling and Predicting Task Success.- Conclusion and Future Directions.

Presents techniques and tools to render SDS “intelligent” and answers on how to make next-generation’s SDS adaptive, user-friendly and emotion-aware

Applies novel research methods about emotion recognition and problem spotting on commercial SDS

Describes how users perceive interactions with an SDS and identifies ways to automatically estimate user satisfaction at arbitrary points in a spoken human-machine interaction