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The MIL Speech Seminar series schedule for Lent Term 2007 is as follows:
| 10th March 2008 | Blaise Thompson (MIL) | Uncertainty and Learning in Spoken Human-Computer Dialogue |
In any spoken dialogue with a computer both speech recognition and semantic
processing errors cause significant decreases in performance. Recent work
has suggested the Partially Observable Markov Decision Process (POMDP) as a
method for overcoming these difficulties. The POMDP model is able to
capture the uncertainty inherent in dialogue and also provides a mechanism
for the system to adapt and learn what to say in which situation. While
effective on small problems the POMDP approach has struggled to scale to
real world dialogues. This talk introduces an approach based on the POMDP
model which does scale. Bayesian Networks are used to implement efficient
belief updates and special function approximation techniques with gradient
based learning provide an effective learning algorithm. Simulations show
that the proposed framework outperforms standard techniques whenever errors
increase
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