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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. . | Letter-Phoneme Alignment An Exploration Sittichai Jiampojamarn and Grzegorz Kondrak Department of Computing Science University of Alberta Edmonton AB T6G 2E8 Canada sj kondrak @cs.ualberta.ca Abstract 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. 1 Introduction Letter-to-phoneme L2P conversion also called grapheme-to-phoneme conversion is the task of predicting the pronunciation of a word given its orthographic form by converting a sequence of letters into a sequence of phonemes. The L2P task plays a crucial role in speech synthesis systems Schroeter et al. 2002 and is an important part of other applications including spelling correction Toutanova and Moore 2001 and speech-to-speech machine translation Engelbrecht and Schultz 2005 . Many data-driven techniques have been proposed for letter-to-phoneme conversion systems including neural networks Sejnowski and Rosenberg 1987 decision trees Black et al. 1998 pronunciation by analogy Marchand and Damper 2000 Hidden Markov Models Taylor 2005 and constraint satisfaction Bosch and Can-isius 2006 . Letter-phoneme alignment is an important step in the L2P task. The training data usually consists of pairs of letter and phoneme sequences which are not aligned. Since there is no explicit information indicating the relationships between individual letter and phonemes these must be inferred by a letter-phoneme alignment algorithm before a prediction model can be trained. The quality of the alignment affects the accuracy of L2P .
