A multistrategy approach to improving pronunciation by analogy
Computational Linguistics
A new algorithm for the alignment of phonetic sequences
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Pronunciation modeling for improved spelling correction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Can syllabification improve pronunciation by analogy of English?
Natural Language Engineering
Joint-sequence models for grapheme-to-phoneme conversion
Speech Communication
Improved morpho-phonological sequence processing with constraint satisfaction inference
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
How do you pronounce your name?: improving G2P with transliterations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
EACL 2012 Proceedings of the EACL 2012 Joint Workshop of LINGVIS & UNCLH
Universal grapheme-to-phoneme prediction over Latin alphabets
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Information Sciences: an International Journal
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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.