ACM TSLP - Special Issue: call for Papers:
“Machine Learning for Robust and Adaptive Spoken Dialogue Systems'

* Submission Deadline 1 July 2010 *
http://tslp.acm.org/specialissues.html

During the last decade, research in the field of Spoken Dialogue
Systems (SDS) has experienced increasing growth, and new applications
include interactive search, tutoring and “troubleshooting” systems,
games, and health agents. The design and optimization of such SDS
requires the development of dialogue strategies which can robustly
handle uncertainty, and which can automatically adapt to different
types of users (novice/expert, youth/senior) and noise conditions
(room/street). New statistical learning techniques are also emerging
for training and optimizing speech recognition, parsing / language
understanding, generation, and synthesis for robust and adaptive
spoken dialogue systems.

Automatic learning of adaptive, optimal dialogue strategies is
currently a leading domain of research. Among machine learning
techniques for spoken dialogue strategy optimization, reinforcement
learning using Markov Decision Processes (MDPs) and Partially
Observable MDPs (POMDPs) has become a particular focus.
One concern for such approaches is the development of appropriate
dialogue corpora for training and testing. However, the small amount
of data generally available for learning and testing dialogue
strategies does not contain enough information to explore the whole
space of dialogue states (and of strategies). Therefore dialogue
simulation is most often required to expand existing datasets and
man-machine spoken dialogue stochastic modelling and simulation has
become a research field in its own right. User simulations for
different types of user are a particular new focus of interest.

Specific topics of interest include, but are not limited to:

 • Robust and adaptive dialogue strategies
 • User simulation techniques for robust and adaptive strategy
learning and testing
 • Rapid adaptation methods
 • Modelling uncertainty about user goals
 • Modelling user’s goal evolution along time
 • Partially Observable MDPs in dialogue strategy optimization
 • Methods for cross-domain optimization of dialogue strategies
 • Statistical spoken language understanding in dialogue systems
 • Machine learning and context-sensitive speech recognition
 • Learning for adaptive Natural Language Generation in dialogue
 • Machine learning for adaptive speech synthesis (emphasis, prosody, etc.)
 • Corpora and annotation for machine learning approaches to SDS
 • Approaches to generalising limited corpus data to build user models
and user simulations
 • Evaluation of adaptivity and robustness in statistical approaches
to SDS and user simulation.

Submission Procedure:
Authors should follow the ACM TSLP manuscript preparation guidelines
described on the journal web site http://tslp.acm.org and submit an
electronic copy of their complete manuscript through the journal
manuscript submission site http://mc.manuscriptcentral.com/acm/tslp.
Authors are required to specify that their submission is intended for
this Special Issue by including on the first page of the manuscript
and in the field “Author’s Cover Letter” the note “Submitted for the
Special Issue of Speech and Language Processing on Machine Learning
for Robust and Adaptive Spoken Dialogue Systems”. Without this
indication, your submission cannot be considered for this Special
Issue.

Schedule:
• Submission deadline : 1 July 2010
• Notification of acceptance: 1 October 2010
• Final manuscript due: 15th November 2010

Guest Editors:
Oliver Lemon, Heriot-Watt University, Interaction Lab, School of
Mathematics and Computer Science, Edinburgh, UK.
Olivier Pietquin, Ecole Supérieure d’Électricité (Supelec), Metz, France.

 http://tslp.acm.org/cfp/acmtslp-cfp2010-02.pdf