Improved spelling recognition using a tree-based fast lexical match

This paper addresses the problem of selecting a name from a very large list using spelling recognition. In order to greatly reduce the computational resources required, we propose a tree-based lexical fast match scheme to select a short list of candidate names. Our system consists of a free letter recognizer, a fast matcher, and a rescoring stage. The letter recognizer uses n-grams to generate an n-best list of letter hypotheses. The fast matcher is a tree that is based on confusion classes, where a confusion class is a group of acoustically similar letters such as the e-set. The fast matcher reduces over 100,000 unique last names to tens or hundreds of candidates. Then the rescoring stage picks the best name using either letter alignment or a constrained grammar. The fast matcher retained the correct name 99.6% of the time and the system retrieved the correct name 97.6% of the time.