Towards Adaptive Spoken Dialog Systems

Alexander Schmitt, Wolfgang Minker

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English

Springer-Verlag New York Inc.
15 October 2014

Imaging systems & technology; Data mining; Speech recognition; Computer vision; User interface design & usability

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.

By: Alexander Schmitt, Wolfgang Minker
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States [Currently unable to ship to USA: see Shipping Info]
Edition: 2013 ed.
Dimensions: Height: 235mm, Width: 155mm, Spine: 14mm
Weight: 4.102kg
ISBN: 9781489991683
ISBN 10: 1489991689
Pages: 254
Publication Date: 15 October 2014
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.