Spoken Language systems are going to have a tremendous impact in all
the real world applications, be it healthcare enquiry, public transportation
system or airline booking system maintaining the language ethnicity for
interaction among users across the globe. These system have the capability
of interacting with the user in di erent languages that the system
supports. Normally when a person interacts with another person there are
many non-verbal clues which guide the dialogue and all the utterances have
a contextual relationship, which manage the dialogue as its mixed by the
two speakers. Human Computer Interaction has a wide impact on the design
of the applications and has become one of the emerging interest area of
the researchers. All of us are witness to an explosive electronic revolution
where lots of gadgets and gizmo's have surrounded us, advanced not only
in power, design, applications but the ease of access or what we call user
friendly interfaces are designed that we can easily use and control all the
functionality of the devices. Since speech is one of the most intuitive form
of interaction that humans use. It provides potential bene ts such as handfree
access to machines, ergonomics and greater e ciency of interaction.
Yet, speech-based interfaces design has been an expert job for a long time.
Lot of research has been done in building real spoken Dialogue Systems
which can interact with humans using voice interactions and help in performing
various tasks as are done by humans. Last two decades have seen
utmost advanced research in the automatic speech recognition, dialogue
management, text to speech synthesis and Natural Language Processing
for various applications which have shown positive results. This dissertation
proposes to apply machine learning (ML) techniques to the problem
of optimizing the dialogue management strategy selection in the Spoken
Dialogue system prototype design. Although automatic speech recognition
and system initiated dialogues where the system expects an answer in the
form of `yes' or `no' have already been applied to Spoken Dialogue Systems(
SDS), no real attempt to use those techniques in order to design a
new system from scratch has been made. In this dissertation, we propose
some novel ideas in order to achieve the goal of easing the design of Spoken
Dialogue Systems and allow novices to have access to voice technologies.
A framework for simulating and evaluating dialogues and learning optimal
dialogue strategies in a controlled Natural Language is proposed. The simulation
process is based on a probabilistic description of a dialogue and
on the stochastic modelling of both arti cial NLP modules composing a
SDS and the user. This probabilistic model is based on a set of parameters
that can be tuned from the prior knowledge from the discourse or learned
from data. The evaluation is part of the simulation process and is based
on objective measures provided by each module. Finally, the simulation
environment is connected to a learning agent using the supplied evaluation
metrics as an objective function in order to generate an optimal behaviour
for the SDS
