Proactive Spoken Dialogue Interaction In Multi Party Environments
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Proactive Spoken Dialogue Interaction in Multi-Party Environments
Author: Petra-Maria Strauß
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-04-06
Proactive Spoken Dialogue Interaction in Multi-Party Environments describes spoken dialogue systems that act as independent dialogue partners in the conversation with and between users. The resulting novel characteristics such as proactiveness and multi-party capabilities pose new challenges on the dialogue management component of such a system and require the use and administration of an extensive dialogue history. In order to assist the proactive spoken dialogue systems development, a comprehensive data collection seems mandatory and may be performed in a Wizard-of-Oz environment. Such an environment builds also the appropriate basis for an extensive usability and acceptance evaluation. Proactive Spoken Dialogue Interaction in Multi-Party Environments is a useful reference for students and researchers in speech processing.
Speech, Image, and Language Processing for Human Computer Interaction: Multi-Modal Advancements
"This book identifies the emerging research areas in Human Computer Interaction and discusses the current state of the art in these areas"--Provided by publisher.
Towards Adaptive Spoken Dialog Systems
Author: Alexander Schmitt
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-09-19
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.