A Practical Application of Evolving Fuzzy-Rule-Based Classifiers for the Development of Spoken Dialog Systems

In this paper we present a statistical approach based on evolving Fuzzy-rule-based (FRB) classifiers for the development of dialog managers for spoken dialog systems. The dialog managers developed by means of our proposal select the next system action by considering a set of dynamic fuzzy rules that are automatically obtained by means of the application of the FRB classification process. Our approach has the main advantage of taking into account the data supplied by the user throughout the complete dialog history without causing scalability problems, also considering confidence measures provided by the recognition and understanding modules. The use of EFS also allows to process streaming data on-line in real time, thus dynamically evolving the structure and operation of the dialog model based on the interaction of the dialog system with its users. We also describe the application of our proposal to develop a dialog system providing railway information.

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

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

[3]  Yorick Wilks,et al.  Some background on dialogue management and conversational speech for dialogue systems , 2011, Comput. Speech Lang..

[4]  Roberto Pieraccini,et al.  The use of belief networks for mixed-initiative dialog modeling , 2000, IEEE Trans. Speech Audio Process..

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

[6]  Steve J. Young,et al.  A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies , 2006, The Knowledge Engineering Review.

[7]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[8]  Hui Ye,et al.  The Hidden Information State Approach to Dialog Management , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

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

[10]  Roberto Pieraccini,et al.  A stochastic model of human-machine interaction for learning dialog strategies , 2000, IEEE Trans. Speech Audio Process..

[11]  Encarna Segarra,et al.  An Online Generated Transducer to Increase Dialog Manager Coverage , 2012, INTERSPEECH.

[12]  Encarna Segarra,et al.  Construction of language models using the morphic generator grammatical inference (MGGI) methodology , 1997, EUROSPEECH.

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

[14]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Steve Young,et al.  The statistical approach to the design of spoken dialogue systems , 2003 .