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- DescriptionIn 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 mograph examines how frequently users show negative emotions in spoken dialog systems and develop vel 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 n-acted recordings several thousand real users from commercial and n-commercial SDS. Additionally, the authors present vel 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, t only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.
- Author(s)Alexander H. W. Schmitt,Wolfgang Minker
- PublisherSpringer-Verlag New York Inc.
- Date of Publication19/09/2012
- SubjectComputing: Professional & Programming
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Note45 black & white tables, biography
- Weight567 g
- Width155 mm
- Height235 mm
- Spine23 mm
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