Andere Kunden interessierten sich auch für
![Estimating Spoken Dialog System Quality with User Models]( "Estimating Spoken Dialog System Quality with User Models")
Estimating Spoken Dialog System Quality with User Models82,99 €![Adaptive Multimodal Interactive Systems]( "Adaptive Multimodal Interactive Systems")
Adaptive Multimodal Interactive Systems74,99 €![Introducing Spoken Dialogue Systems into Intelligent Environments]( "Introducing Spoken Dialogue Systems into Intelligent Environments")
Introducing Spoken Dialogue Systems into Intelligent Environments74,99 €![Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop]( "Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop")
Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop147,99 €![Spoken Dialogue Systems Technology and Design]( "Spoken Dialogue Systems Technology and Design")
Spoken Dialogue Systems Technology and Design147,99 €![Natural Language Processing and Information Systems]( "Natural Language Processing and Information Systems")
Natural Language Processing and Information Systems37,99 €![Foundations of Intelligent Systems]( "Foundations of Intelligent Systems")
Foundations of Intelligent Systems37,99 €
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.
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.