Speech recognition without a lexicon - bridging the gap between graphemic and phonetic systems

Modern speech recognizers rely on three core components: an acoustic model, a language model, and a pronunciation lexicon. In order to expand speech recognition capability to lowresource languages and domains, techniques to peel away the expert knowledge required to craft these three components have been growing in popularity. In this paper, we present a method for automatically learning a weighted pronunciation lexicon in a data-driven fashion without assuming the existence of any phonetic lexicon whatsoever. Given an initial grapheme acoustic model, our method utilizes a novel technique for semiconstrained acoustic unit decoding, which is used to help train a letter to sound (L2S) model. The L2S model is then used in conjunction with a Pronunciation Mixture Model (PMM) to infer a pronunciation lexicon. We evaluate our method on English as well as Lao and Haitian, two low-resource languages featured in the IARPA Babel program.