Paper Info
Title
Modeling letter-to-phoneme conversion as a phrase based statistical machine translation problem with minimum error rate training
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.
Year
Venue
Keywords
2009
HLT-NAACL (Student Research Workshop and Doctoral Consortium)
important role,statistical machine translation technique,language independent letter-to-phoneme conversion,popular phrase,overall improvement,l2p task,letter-to-phoneme conversion,difficulty level,difficult task,statistical machine translation problem,minimum error rate training
Field
DocType
Citations 
Computer science,Machine translation,Word error rate,Phrase,Speech recognition,Natural language processing,Artificial intelligence,Machine learning
Conference
12
PageRank 
References 
Authors
0.64
22
3
Name
Order
Citations
PageRank
Taraka Rama13210.60
Anil Kumar Singh217343.15
Sudheer Kolachina3323.67