Letter-phoneme alignment is usually generated by a straightforward application of the EM algorithm. We explore several alternative alignment methods that employ phonetics, integer programming, and sets of constraints, and propose a novel approach of refining the EM alignment by aggregation of best alignments. We perform both intrinsic and extrinsic evaluation of the assortment of methods. We show that our proposed EM-Aggregation algorithm leads to the improvement of the state of the art in letter-to-phoneme conversion on several different data sets. .
