Iterative Grapheme-to-Phoneme Alignment for the Training of WFST-based Phonetic Conversion

In this paper we propose an algorithm for grapheme-to-phoneme (G2P) alignment. Such alignment is needed mainly for the data-driven training of G2P conversion tools. Our approach utilizes a given phonetic alphabet and a set of given orthographic-phonetic word pairs as a source of prior knowledge. The development data are taken from a manually created pronunciation lexicon for a large vocabulary speech recognition system for Czech. The alignment method is based on extended Minimum Edit Distance algorithm. Moreover, we propose an approach to avoid the creation of reference alignments - we evaluate the improvements through a specially designed G2P converter, i.e. we compare the phonetic transcription directly to a set of test orthographic-phonetic word pairs. Results of our approach are comparable or even slightly better than the state-of-the-art.