Memory-based Reasoning Applied to English Pronunciation

Craig W. Stanfill

Memory-based Reasoning is a paradigm for AI in which best-match recall from memory is the primary inference mechanism. In its simplest form, it is a method of solving the inductive inference (learning) problem. The primary topics of this paper are a simple memory-based reasoning algorithm, the problem of pronouncing english words, and MBRtalk, a program which uses memory-based reasoning to solve the pronunciation problem. Experimental results demonstrate the properties of the algorithm as training-set size is varied, as distracting information is added, and as noise is added to the data.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.