ISBN: 9781461445937
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ISBN: 9781461445937
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ISBN: 9781461445937
In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Sp… Plus…
2012, ISBN: 9781461445937
eBooks, eBook Download (PDF), 2013, In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recogniti… Plus…
ISBN: 9781461445937
In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Sp… Plus…
ISBN: 9781461445937
*Towards Adaptive Spoken Dialog Systems* / pdf eBook für 96.49 € / Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet Medien > Bücher nein eBook als pdf eBooks > Sachthem… Plus…
2012, ISBN: 9781461445937
eBooks, eBook Download (PDF), 2013, [PU: Springer New York], Springer New York, 2012
Données bibliographiques du meilleur livre correspondant
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Informations détaillées sur le livre - Towards Adaptive Spoken Dialog Systems
EAN (ISBN-13): 9781461445937
ISBN (ISBN-10): 1461445930
Date de parution: 2012
Editeur: Springer New York
251 Pages
Langue: eng/Englisch
Livre dans la base de données depuis 2012-08-09T00:01:44+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-03-23T14:53:23+01:00 (Zurich)
ISBN/EAN: 9781461445937
ISBN - Autres types d'écriture:
1-4614-4593-0, 978-1-4614-4593-7
Autres types d'écriture et termes associés:
Auteur du livre: wolfgang schmit, alexander wolf, mink, schmitt, minke
Titre du livre: dialog, spoke, system, alexander wolfgang
Données de l'éditeur
Auteur: Alexander Schmitt; Wolfgang Minker
Titre: Towards Adaptive Spoken Dialog Systems
Editeur: Springer; Springer US
254 Pages
Date de parution: 2012-09-19
New York; NY; US
Imprimé / Fabriqué en
Langue: Anglais
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XIV, 254 p.
EA; E107; eBook; Nonbooks, PBS / 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; B; Signal, Image and Speech Processing; 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; Engineering; Digitale Signalverarbeitung (DSP); Data Mining; Wissensbasierte Systeme, Expertensysteme; Mensch-Computer-Interaktion; Bildverarbeitung; BB
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.
Monitoring Adaptive Spoken Dialog SystemsIntroduction.- 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.
Monitoring Adaptive Spoken Dialog SystemsPresents 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
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