A NEURAL SYSTEM OF PHONEMATIC TRANSFORMATION

Abstract

A common task in speech processing for which neural networks are widely employed is text-to-phoneme conversion. In this paper we propose a novel solution to this problem by combining a multilayer neural network and a modular hybrid system that uses basic rules to subdivide the original problem into easier tasks which are then solved by dedicated neural networks. A hybrid solution can be more rapidly constructed than a single net solution, and is easily extendable. Input data representation is also discussed. A voting committee concept is used to enhance generalization abilities of the system. Efficiency of the proposed systems is compared.