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We describe an iterative recognition strategy that can be used to improve vastly the performance of a speech recognition system when the speech pertains to structured information that can be looked up in a database. The framework that we present is designed to extract specific fields of interest from the speech signal during each iteration, query a database using these fields, and thereby construct the hypothesis space for searching during the next iteration. The architecture has been found to be significantly useful in applications such as spoken address recognition where a proof of concept and a demonstration system had been developed. We also present results on a small test set to compare the performance of the described system with the more common baseline approach.