A proposal for improving spoken dialog systems using context information fusion

Context information fusion allow developing systems that provides large quantities of information obtained from network sensors with heterogeneous characteristics, that needs to be efficiently processed. During the last years, this field has become increasingly relevant due to the growing importance and rapid advances in sensor technology that provide information from the environment. Many works has incorporate context into a fusion data framework, showing the improvement of accuracy by integrating context, others proved the usefulness of context to improve classification task by using multiple classifier system. This paper proposes an improvement of a spoken dialog system using context information fusion. An effective application of this approach is described for a spoken dialog system providing railway information.

[1]  Galina L. Rogova,et al.  Designing ontologies for higher level fusion , 2009, Inf. Fusion.

[2]  Pong C. Yuen,et al.  Automatic Acquisition of Context Models and its Application to Video Surveillance , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  Juan Luis Castro,et al.  A case based reasoning model for multilingual language generation in dialogues , 2012, Expert Syst. Appl..

[4]  Galina L. Rogova.  Context-awareness in crisis management , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[5]  John McCarthy,et al.  Notes on Formalizing Context , 1993, IJCAI.

[6]  John Mourjopoulos,et al.  Automatic speech recognition performance in different room acoustic environments with and without dereverberation preprocessing , 2013, Comput. Speech Lang..

[7]  Feng Gao,et al.  Spoken language understanding using weakly supervised learning , 2010, Comput. Speech Lang..

[8]  Ramón López-Cózar,et al.  A domain-independent statistical methodology for dialog management in spoken dialog systems , 2014, Comput. Speech Lang..

[9]  Javier Bajo,et al.  Intelligent Systems in Context-Based Distributed Information Fusion , 2013, Int. J. Distributed Sens. Networks.

[10]  Christopher Bowman,et al.  Adaptive context discovery and exploitation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[11]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[12]  Lior Rokach,et al.  Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography , 2009, Comput. Stat. Data Anal..

[13]  S. Renals,et al.  Spoken dialogue interfaces for older people , 2012 .

[14]  Stuart C. Shapiro,et al.  Evaluating spreading activation for soft information fusion , 2011, 14th International Conference on Information Fusion.

[15]  R. Polikar,et al.  Multiple classifier systems for multisensor data fusion , 2006, Proceedings of the 2006 IEEE Sensors Applications Symposium, 2006..

[16]  Wolfgang Minker,et al.  Introducing Spoken Dialogue Systems into Intelligent Environments , 2012 .

[17]  Eduardo F. Nakamura,et al.  Information fusion for wireless sensor networks: Methods, models, and classifications , 2007, CSUR.

[18]  José M. Molina López,et al.  Context-based multi-level information fusion for harbor surveillance , 2015, Inf. Fusion.

[19]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[20]  Juan M. Corchado,et al.  Context-based information fusion , 2015, Inf. Fusion.

[21]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[22]  Roberto Pieraccini.  The Voice in the Machine: Building Computers That Understand Speech , 2012 .

[23]  Hai-Hui Wang,et al.  Multisensor Information Fusion Application to SAR Data Classification , 2006 .

[24]  James Llinas.  A Survey and Analysis of Frameworks and Framework Issues for Information Fusion Applications , 2010, HAIS.

[25]  Miguel A. Patricio,et al.  Strategies and techniques for use and exploitation of Contextual Information in high-level fusion architectures , 2010, 2010 13th International Conference on Information Fusion.

[26]  Wilson Jeberson,et al.  Survey of Context Information Fusion for Sensor Networks based Ubiquitous Systems , 2013, ArXiv.

[27]  RokachLior.  Taxonomy for characterizing ensemble methods in classification tasks , 2009 .

[28]  Emilio Corchado,et al.  A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.

[29]  Peter Reinartz,et al.  Multi-sensor data fusion for urban area classification , 2011, 2011 Joint Urban Remote Sensing Event.

[30]  Misha Pavel,et al.  Robust image recognition by fusion of contextual information , 2002, Inf. Fusion.

[31]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[32]  Thomas Hempel.  Usability of Speech Dialog Systems: Listening to the Target Audience , 2010 .

[33]  David Griol,et al.  A statistical approach to spoken dialog systems design and evaluation , 2008, Speech Commun..

[34]  Gian Luca Foresti,et al.  Context in fusion: Some considerations in a JDL perspective , 2013, Proceedings of the 16th International Conference on Information Fusion.

[35]  Erik Blasch,et al.  Adaptive context assessment and context management , 2014, 17th International Conference on Information Fusion (FUSION).

[36]  Jesús García,et al.  Context-based Information Fusion: A survey and discussion , 2015, Inf. Fusion.

[37]  Ludmila I. Kuncheva,et al.  Feature Subsets for Classifier Combination: An Enumerative Experiment , 2001, Multiple Classifier Systems.

[38]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.