Automatic Generation of a Pronunciation Dictionary with Rich Variation Coverage Using SMT Methods

Abstract

Constructing a pronunciation lexicon with variants in a fully automatic and language-independent way is a challenge, with many uses in human language technologies. Moreover, with the growing use of web data, there is a recurrent need to add words to existing pronunciation lexicons, and an automatic method can greatly simplify the effort required to generate pronunciations for these out-of-vocabulary words. In this paper, a machine translation approach is used to perform grapheme-to-phoneme (g2p) conversion, the task of finding the pronunciation of a word from its written form. Two alternative methods are proposed to derive pronunciation variants. In the first case, an n-best pronunciation list is extracted directly from the g2p converter. The second is a novel method based on a pivot approach, traditionally used for the paraphrase extraction task, and applied as a post-processing step to the g2p converter. The performance of these two methods is compared under different training conditions. The range of applications which require pronunciation lexicons is discussed and the generated pronunciations are further tested in some preliminary automatic speech recognition experiments.