Reducing the Annotation Effort for Letter-to-Phoneme Conversion

Letter-to-phoneme (L2P) conversion is the process of producing a correct phoneme sequence for a word, given its letters. It is often desirable to reduce the quantity of training data -- and hence human annotation -- that is needed to train an L2P classifier for a new language. In this paper, we confront the challenge of building an accurate L2P classifier with a minimal amount of training data by combining several diverse techniques: context ordering, letter clustering, active learning, and phonetic L2P alignment. Experiments on six languages show up to 75% reduction in annotation effort.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Kenneth Dwyer, Grzegorz Kondrak
• Keywords: ACL 2009, Computational Linguistics, Correct Phoneme Sequence, L2P Classifier, Training Data