A hybrid neural network/rule based system for bilingual text-to-phoneme mapping

Abstract

Text-to-phoneme (TTP) mapping is a preliminary step in text-to-speech synthesis and it affects the naturalness and understandability of synthetic speech. In this paper, we propose a hybrid neural network/rule based system for bilingual text-to-phoneme mapping. Our system uses three neural networks and a simple rule to perform the phoneme transcription. The first network is trained to convert the letters from the first language into their corresponding phonemes, the second one is used to obtain the phonemes for the second language whereas the third neural network together with a simple rule is responsible of the language recognition. The proposed approach can be easily extended for multilingual applications when more neural networks are introduced. Simulations performed on a bilingual dictionary (English+French) show the improvements in terms of phoneme accuracy of our method against the approach that uses a single neural network for multilingual TTP