Automatic construction of unique signatures and confusable sets for natural language directory assistance applications

Abstract

This paper addresses the problem of building natural language based grammars and language models for directory assistance applications that use automatic speech recognition. As input, one is given an electronic version of a standard phone book, and the output is a grammar or language model that will accept all the ways in which one might ask for a particular listing. We focus primarily on the problem of processing listings for businesses and government offices, but our techniques can be used to speech-enable other kinds of large listings (like book titles, catalog entries, etc.). We have applied these techniques to the business listings of a state in the Midwestern United States, and we present highly encouraging recognition results.