Bringing together commercial and academic perspectives for the development of intelligent AmI interfaces

The users of Ambient Intelligence systems expect an intelligent behavior from their environment, receiving adapted and easily accessible services and functionality. This can only be possible if the communication between the user and the system is carried out through an interface that is simple (i.e. which does not have a steep learning curve), fluid (i.e. the communication takes place rapidly and effectively), and robust (i.e. the system understands the user correctly). Natural language interfaces such as dialog systems combine the previous three requisites, as they are based on a spoken conversation between the user and the system that resembles human communication. The current industrial development of commercial dialog systems deploys robust interfaces in strictly defined application domains. However, commercial systems have not yet adopted the new perspective proposed in the academic settings, which would allow straightforward adaptation of these interfaces to various application domains. This would be highly beneficial for their use in AmI settings as the same interface could be used in varying environments. In this paper, we propose a new approach to bridge the gap between the academic and industrial perspectives in order to develop dialog systems using an academic paradigm while employing the industrial standards, which makes it possible to obtain new generation interfaces without the need for changing the already existing commercial infrastructures. Our proposal has been evaluated with the successful development of a real dialog system that follows our proposed approach to manage dialog and generates code compliant with the industry-wide standard VoiceXML.

[1]  Pascal Poupart,et al.  Partially Observable Markov Decision Processes with Continuous Observations for Dialogue Management , 2008, SIGDIAL.

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

[3]  David Traum,et al.  The Information State Approach to Dialogue Management , 2003 .

[4]  Tim Paek,et al.  Toward Evaluation that Leads to Best Practices: Reconciling Dialog Evaluation in Research and Industry , 2007, Proceedings of the Workshop on Bridging the Gap Academic and Industrial Research in Dialog Technologies - NAACL-HLT '07.

[5]  Richard Fikes,et al.  The role of frame-based representation in reasoning , 1985, CACM.

[6]  Wolfgang Minker,et al.  A framework for adapting interactive systems to user behavior , 2010, J. Ambient Intell. Smart Environ..

[7]  James R. Glass,et al.  Multilingual language generation across multiple domains , 1994, ICSLP.

[8]  Steve Young,et al.  Simulation of human-machine dialogues , 1999 .

[9]  Steve Young,et al.  Statistical User Simulation with a Hidden Agenda , 2007, SIGDIAL.

[10]  Salvador España Boquera,et al.  Efficient BP Algorithms for General Feedforward Neural Networks , 2007, IWINAC.

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  Steve J. Young,et al.  Partially observable Markov decision processes for spoken dialog systems , 2007, Comput. Speech Lang..

[13]  David Griol,et al.  Acquiring and Evaluating a Dialog Corpus through a Dialog Simulation Technique , 2007, SIGdial.

[14]  Marilyn A. Walker,et al.  Reinforcement Learning for Spoken Dialogue Systems , 1999, NIPS.

[15]  Michael F. McTear,et al.  Book Review , 2005, Computational Linguistics.

[16]  Jonathan J. Cadiz,et al.  "Let There Be Light": Examining Interfaces for Homes of the Future , 2001, INTERACT.

[17]  Hans W. Guesgen,et al.  Exploring the responsibilities of single-inhabitant Smart Homes with Use Cases , 2010, J. Ambient Intell. Smart Environ..

[18]  Victor Zue,et al.  YINHE: a Mandarin Chinese version of the GALAXY system , 1997, EUROSPEECH.

[19]  Kallirroi Georgila,et al.  Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems , 2005, SIGDIAL.

[20]  Joelle Pineau,et al.  Spoken Dialogue Management Using Probabilistic Reasoning , 2000, ACL.

[21]  Ramón López-Cózar,et al.  The role of spoken language dialogue interaction in intelligent environments , 2009, J. Ambient Intell. Smart Environ..

[22]  Konrad Scheffler,et al.  Probabilistic simulation of human-machine dialogues , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[23]  Olivier Pietquin,et al.  Comparing ASR modeling methods for spoken dialogue simulation and optimal strategy learning , 2005, INTERSPEECH.

[24]  Fulvio Corno,et al.  What would you ask to your home if it were intelligent? Exploring user expectations about next-generation homes , 2011, J. Ambient Intell. Smart Environ..

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

[26]  Pablo A. Haya,et al.  A Dynamic Spoken Dialogue Interface for Ambient Intelligence Interaction , 2010, Int. J. Ambient Comput. Intell..

[27]  Hui Ye,et al.  Training a real-world POMDP-based Dialog System , 2007, HLT-NAACL 2007.

[28]  Roberto Pieraccini,et al.  Technical Support Dialog Systems:Issues, Problems, and Solutions , 2007, HLT-NAACL 2007.

[29]  Kallirroi Georgila,et al.  User simulation for spoken dialogue systems: learning and evaluation , 2006, INTERSPEECH.

[30]  Steve Young,et al.  Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning , 2002 .

[31]  Hua Ai,et al.  Comparing Spoken Dialog Corpora Collected with Recruited Subjects versus Real Users , 2007, SIGDIAL.

[32]  Marilyn A. Walker,et al.  Evaluating competing agent strategies for a voice email agent , 1997, EUROSPEECH.

[33]  Herbert H. Clark,et al.  Grounding in communication , 1991, Perspectives on socially shared cognition.

[34]  Roberto Pieraccini,et al.  Automating spoken dialogue management design using machine learning: An industry perspective , 2008, Speech Commun..

[35]  Ramón López-Cózar,et al.  Multimodal Dialogue for Ambient Intelligence and Smart Environments , 2010, Handbook of Ambient Intelligence and Smart Environments.

[36]  Michael F. McTear,et al.  Spoken Dialogue Technology , 2004, Springer London.

[37]  Ramón López-Cózar,et al.  A Methodology for Learning Optimal Dialog Strategies , 2010, TSD.

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

[39]  Ramón López-Cózar,et al.  Using knowledge of misunderstandings to increase the robustness of spoken dialogue systems , 2010, Knowl. Based Syst..

[40]  Jason D. Williams,et al.  The best of both worlds: unifying conventional dialog systems and POMDPs , 2008, INTERSPEECH.

[41]  Steve J. Young,et al.  Error simulation for training statistical dialogue systems , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[42]  Oliver Lemon,et al.  Learning multi-goal dialogue strategies using reinforcement learning with reduced state-action spaces , 2006, INTERSPEECH.

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

[44]  David Suendermann-Oeft,et al.  Are We There Yet? Research in Commercial Spoken Dialog Systems , 2009, TSD.

[45]  Joseph Polifroni,et al.  Galaxy-II as an Architecture for Spoken Dialogue Evaluation , 2000, LREC.

[46]  Thierry Dutoit,et al.  A probabilistic framework for dialog simulation and optimal strategy learning , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[47]  Boris E. R. de Ruyter,et al.  Benefits of Social Intelligence in Home Dialogue Systems , 2005, INTERACT.

[48]  Ramón López-Cózar,et al.  Relations between de-facto criteria in the evaluation of a spoken dialogue system , 2008, Speech Commun..

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

[50]  Hui Ye,et al.  Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System , 2007, NAACL.

[51]  Kallirroi Georgila,et al.  Learning user simulations for information state update dialogue systems , 2005, INTERSPEECH.

[52]  David Griol,et al.  Learning the structure of human-computer and human-human dialogs , 2009, INTERSPEECH.

[53]  Peter A. Heeman.  Combining Reinformation Learning with Information-State Update Rules , 2007, HLT-NAACL.

[54]  S. Singh,et al.  Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System , 2011, J. Artif. Intell. Res..

[55]  Stephanie Seneff,et al.  Lexical stress modeling for improved speech recognition of spontaneous telephone speech in the jupiter domain , 2001, INTERSPEECH.

[56]  Nuno J. Mamede,et al.  Ambient Intelligence Interaction via Dialogue Systems , 2010 .

[57]  Roberto Pieraccini,et al.  User modeling for spoken dialogue system evaluation , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.

[58]  H. Cuayahuitl,et al.  Human-computer dialogue simulation using hidden Markov models , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..

[59]  Eric Horvitz,et al.  Conversation as Action Under Uncertainty , 2000, UAI.

[60]  Jennifer Balogh,et al.  Voice User Interface Design , 2004 .