Data-oriented methods for grapheme-to-phoneme conversion
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
"Derivational" paradigms in morphonology
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Saussurian analogy: a theoretical account and its application
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
A multistrategy approach to improving pronunciation by analogy
Computational Linguistics
Pronunciation by analogy in normal and impaired readers
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Journal of Artificial Intelligence Research
Data driven approaches to speech and language processing
Nonlinear Speech Modeling and Applications
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We present and experimentally evaluate a new model of prounciation by analogy: the paradigmatic cascades model. Given a pronunciation lexicon, this algorithm first extracts the most productive paradigmatic mappings in the graphemic domain, and pairs them statistically with their correlate(s) in the phonemic domain. These mappings are used to search and retrieve in the lexical database the most promising analog of unseen words. We finally apply to the analogs pronunciation the correlated series of mappings in the phonemic domain to get the desired pronunciation.