Towards Adaptive Spoken Dialog Systems - encuadernado, tapa blanda

Libro en la base de datos desde 2009-09-04T18:11:26-05:00 (Mexico City)
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ISBN/EAN: 1461445922

ISBN - escritura alterna:
1-4614-4592-2, 978-1-4614-4592-0
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: alexander wolf, mink, maria schmitt, wolfgang will, minke, wolfgang bücher
Título del libro: dialog, spoke, system, alexander wolfgang, schmitt buch

Datos del la editorial

Autor: Alexander Schmitt; Wolfgang Minker
Título: Towards Adaptive Spoken Dialog Systems
Editorial: Springer; Springer US
254 Páginas
Año de publicación: 2012-09-19
New York; NY; US
Impreso en
Peso: 5,266 kg
Idioma: Inglés
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XIV, 254 p.

BB; Signal, Image and Speech Processing; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Elektronik; Verstehen; Adaptive Spoken Dialogue Systems; Dialogue Systems; Emotion Recognition; Interaction Modeling; Linguistic Modeling; Machine Learning; Paralinguistic Modeling; Platform Development; SRI; Wolfgang Minker; Data Mining and Knowledge Discovery; User Interfaces and Human Computer Interaction; Computer Imaging, Vision, Pattern Recognition and Graphics; Signal, Speech and Image Processing; Data Mining and Knowledge Discovery; User Interfaces and Human Computer Interaction; Computer Imaging, Vision, Pattern Recognition and Graphics; Digitale Signalverarbeitung (DSP); Data Mining; Wissensbasierte Systeme, Expertensysteme; Mensch-Computer-Interaktion; Bildverarbeitung; BC; EA

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.

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.

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