Voice-Rate is an experimental dialog system

through which a user can call to get product

information. In this paper, we describe

an optimal dialog management algorithm for

Voice-Rate. Our algorithm uses a POMDP

framework, which is probabilistic and captures

uncertainty in speech recognition and

user knowledge. We propose a novel method

to learn a user knowledge model from a review

database. Simulation results show that the

POMDP system performs significantly better

than a deterministic baseline system in terms

of both dialog failure rate and dialog interaction

time. To the best of our knowledge, our

work is the first to show that a POMDP can

be successfully used for disambiguation in a

complex voice search domain like Voice-Rate.

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In  In Proceedings of SIGdial

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