Modeling letter-to-phoneme conversion as a phrase based statistical machine translation problem with minimum error rate training

Authors:
Taraka Rama;Anil Kumar Singh;Sudheer Kolachina
Affiliations:
IIIT, Hyderabad, India;IIIT, Hyderabad, India;IIIT, Hyderabad, India
Venue:
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Year:
2009

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Abstract

Letter-to-phoneme conversion plays an important role in several applications. It can be a difficult task because the mapping from letters to phonemes can be many-to-many. We present a language independent letter-to-phoneme conversion approach which is based on the popular phrase based Statistical Machine Translation techniques. The results of our experiments clearly demonstrate that such techniques can be used effectively for letter-to-phoneme conversion. Our results show an overall improvement of 5.8% over the baseline and are comparable to the state of the art. We also propose a measure to estimate the difficulty level of L2P task for a language.