Paper: Reducing the Annotation Effort for Letter-to-Phoneme Conversion

ACL ID P09-1015
Title Reducing the Annotation Effort for Letter-to-Phoneme Conversion
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

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 anno- tation — that is needed to train an L2P classifier for a new language. In this pa- per, we confront the challenge of building an accurate L2P classifier with a minimal amount of training data by combining sev- eral diverse techniques: context ordering, letter clustering, active learning, and pho- netic L2P alignment. Experiments on six languagesshowupto75%reductioninan- notation effort.