Paper: Automatic Syllabification with Structured SVMs for Letter-to-Phoneme Conversion

ACL ID P08-1065
Title Automatic Syllabification with Structured SVMs for Letter-to-Phoneme Conversion
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We present the rst English syllabi cation system to improve the accuracy of letter-to- phoneme conversion. We propose a novel dis- criminative approach to automatic syllabi ca- tion based on structured SVMs. In comparison with a state-of-the-art syllabi cation system, we reduce the syllabi cation word error rate for English by 33%. Our approach also per- forms well on other languages, comparing fa- vorably with published results on German and Dutch.