ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Inducing probabilistic syllable classes using multivariate clustering
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
On the syllabification of phonemes
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Improving syllabification models with phonotactic knowledge
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Representational bias in unsupervised learning of syllable structure
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Syllabification algorithm based on syllable rules matching for Malay language
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
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We present a phonological probabilistic context-free grammar, which describes the word and syllable structure of German words. The grammar is trained on a large corpus by a simple supervised method, and evaluated on a syllabification task achieving 96.88% word accuracy on word tokens, and 90.33% on word types. We added rules for English phonemes to the grammar, and trained the enriched grammar on an English corpus. Both grammars are evaluated qualitatively showing that probabilistic context-free grammars can contribute linguistic knowledge to phonology. Our formal approach is multilingual, while the training data is language-dependent.