Grapheme to phoneme translation using conditional random fields with re-ranking

Abstract

Grapheme to phoneme (G2P) translation is an important part of many applications including text to speech, automatic speech recognition, and phonetic similarity matching. Although G2P models have been studied thoroughly in the literature, we propose a G2P system which is optimized for producing a high-quality top-k list of candidate pronunciations for an input grapheme string. Our pipeline approach uses Conditional Random Fields (CRF) to predict phonemes from graphemes and a discriminative re-ranker, which incorporates information from previous stages in the pipeline with a graphone language model to construct a high-quality ranked list of results. We evaluate our findings against the widely used CMUDict dataset and demonstrate competitive performance with state-of-the-art G2P methods. Additionally, using entries with multiple valid pronunciations, we show that our re-ranking approach out-performs ranking using only a smoothed graphone language model, a technique employed by many recent publications. Lastly, we released our system as an open-source G2P toolkit available at http:// bit.ly/83yysKL.