Abstract:

This paper presents an experiment comparing a new name-pronunciation system, Anapron, with seven existing systems: three state-of-the-art commercial systems (from Bellcore, Bell Labs, and DEC), two variants of a machine-learning system (NETtalk), and two humans. Anapron works by combining rule-based and case-based reasoning. It is based on the idea that it is much easier to improve a rule-based system by adding case-based reasoning to it than by tuning the rules to deal with every exception. In the experiment described here, Anapron used a set of rules adapted from MITalk and elementary foreign-language textbooks, and a case library of 5000 names. With these components -- which required relatively little knowledge engineering -- Anapron was found to perform almost at the level of the commercial systems, and significantly better than the two versions of NETtalk.